Robust Information Feature Pyramid Networks for Low-Visible Object Detection

Jie Hua; Zhongyuan Wang; Xin Tian; Jinsheng Xiao; Gang Wu; Tao Lu; Jiayi Ma

doi:10.1109/JAS.2026.126065

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 18.3, Top 1 (SCI Q1)

CiteScore: 28.2, Top 1% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > In Press, Accepted Manuscript

J. Hua, Z. Wang, X. Tian, J. Xiao, G. Wu, T. Lu, and J. Ma, IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126065

Citation:

J. Hua, Z. Wang, X. Tian, J. Xiao, G. Wu, T. Lu, and J. Ma, IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126065

J. Hua, Z. Wang, X. Tian, J. Xiao, G. Wu, T. Lu, and J. Ma, IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126065

Citation:

J. Hua, Z. Wang, X. Tian, J. Xiao, G. Wu, T. Lu, and J. Ma, IEEE/CAA J. Autom. Sinica, early access, 2026. doi: 10.1109/JAS.2026.126065

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Robust Information Feature Pyramid Networks for Low-Visible Object Detection

doi: 10.1109/JAS.2026.126065

Funds: This research was funded by the National Natural Science Foundation of China (62371350, 62471338), Key Science and Technology Research Project of Xinjiang Production and Construction Corps (2025AB029), Hubei Key Research and Development Program (2025BAB0004), and Central Guidance for Local Science and Technology Development Fund (ZYYD2025QY19)

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Author Bio:
Jie Hua received his B.S. and M.S. degrees from the School of Automation, Wuhan University of Technology, Wuhan, China in 2019 and in 2022 respectively. Now, he is pursuing his Ph.D. degree at the School of Computer Science, Wuhan University. His research field is object detection and computer vision

Zhongyuan Wang (Senior Member, IEEE) received his Ph.D. degree in communication and information system from Wuhan University, Wuhan, China, in 2008. He is currently a professor with the School of Computer Science, Wuhan University, Wuhan, China. He has been directing four projects funded by the National Natural Science Foundation of China. He has authored or co-authored over 80 refereed journal and conference papers and has been granted more than 30 invention patents. His research interests include biometrics and computer vision

Xin Tian (Member, IEEE) received his B.S. degree from the Department of Electronic Science and Technology and his Ph.D. degree from the Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, China, in 2004 and 2010, respectively. He is currently an Associate Professor with the School of Electronic Information, Wuhan University, Wuhan, China. His current research interests include computational imaging, image fusion, dictionary learning, and sparse coding. He was the recipient of many awards, including the First Prize for Scientific and Technological Progress in Surveying and Mapping, in China and the First Prize for Excellent Achievements of Information Technology in Electric Power Industry of China

Jinsheng Xiao (Member, IEEE) received his Ph.D. degree in computational mathematics from the School of Mathematics, Wuhan University, Wuhan, China, in 2001. From 2001 to 2004, he was a Research Associate with the Institution of Multimedia Network Communication and a Lecturer of Communication Engineering with the School of Electronic Information, from 2004 to 2008, with Wuhan University, where he has been an Associate Professor of Information and Communication Engineering, since 2008. From August 2014 to August 2015, he was the Visiting Scholar with the Department of Computer Science, University of California, Santa Barbara, CA, USA. He has authored more than 50 articles. His research interests include video and image processing and computer vision

Gang Wu received his M.S. degree from Qinghai Normal University in 2007. He is a Professor at the College of Cyber Security, Tarim University, Alar, China. His research focuses on image/video processing and network information security

Tao Lu (Member, IEEE) received his B.S. and M.S. degrees from the School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, China, in 2003 and 2008, respectively, and his Ph.D. degree from the National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, in 2013. He was a Post-Doctoral Fellow with the Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA, from 2015 to 2017. He is currently an Associate Professor with the School of Computer Science and Engineering, Wuhan Institute of Technology, and a Research Member with Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan. His research interests include image/video processing, computer vision, and artificial intelligence

Jiayi Ma (Senior Member, IEEE) received his B.S. degree in information and computing science and his Ph.D. degree in control science and engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2008 and 2014, respectively. He is currently a Professor at the Electronic Information School, Wuhan University, Wuhan, China. He has authored or coauthored more than 200 refereed journals and conference papers, including IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)/IEEE Transactions on Image Processing (TIP), International Journal of Computer Vision (IJCV), Computer Vision and Pattern Recognition Conference (CVPR), International Conference on Computer Vision, and European Conference on Computer Vision. His research interests include computer vision, machine learning, and pattern recognition., Dr. Ma has been identified in the 2019–2022 Highly Cited Researcher lists from the Web of Science Group. He is an Area Editor of Information Fusion and an Associate Editor of Neurocomputing, Sensors, and Entropy
Corresponding author: Zhongyuan Wang, e-mail: wzy_hope@163.com; Gang Wu, e-mail: 120070034@taru.edu.cn
Received Date: 2025-10-27
Revised Date: 2026-02-02
Accepted Date: 2026-04-07

Available Online: 2026-09-30

Abstract

Abstract

Detecting low-visible objects with missing contours, such as dark and foggy objects, presents challenges in computer vision. Previous methods have enhanced feature representation by training on synthetic datasets to improve image quality. However, the heavy reliance on paired and high-quality datasets renders data acquisition prohibitively expensive. In this work, we focus on low-quality images and propose a novel plug-and-play feature pyramid network named RIFPN, which can extract robust information. We first propose a novel depth-first search convolution (DFSconv) that progressively predicts regions containing effective features, enabling the capture of low-visibility object features across distant, cross-regional areas. We subsequently propose the Feature Integration Module (FIM), which projects multi-scale features to the same resolution and enables cross-level feature integration through DFSconv, achieving efficient interaction within the feature pyramid. Finally, we introduce the Feature Distribution Module (FDM), which has a varying feature search radius and adapts its search range based on feature resolution. This supplements low-visibility objects and provides more robust multi-scale features. Extensive experiments demonstrate that the robust information extracted using RIFPN significantly improves the detection accuracy of low-visible objects, including dark object detection (+2.2 AP on ExDark and +1.3 AP on DarkFace) and foggy object detection (+1.9 mAP on Foggy Cityscapes and 0.9 AP on HazyDet). In addition, our RIFPN, as a feature pyramid structure, improves 2.3 and 1.7 APs in the general object detection and instance segmentation tasks, respectively.
- Cross-scale feature interaction,
- depth-first search,
- feature integration and distribution,
- low-visible object detection,
- robust information

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References(93)

References

[1]	Y. Han, L. Wang, Y. Wang, and Z. Geng, “Intelligent small sample defect detection of concrete surface using novel deep learning integrating improved YOLOv5,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 545–547, Feb. 2024. doi: 10.1109/JAS.2023.124035
[2]	H. Xu, J. Ma, Y. Yuan, H. Zhang, X. Tian, and X. Guo, “More than lightening: A self-supervised low-light image enhancement method capable for multiple degradations,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 622–637, Mar. 2024. doi: 10.1109/JAS.2024.124263
[3]	C. Pan, J. Peng, and Z. Zhang, “Depth-guided vision transformer with normalizing flows for monocular 3D object detection,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 673–689, Mar. 2024. doi: 10.1109/JAS.2023.123660
[4]	C. Li, C. Guo, and C. C. Loy, “Learning to enhance low-light image via zero-reference deep curve estimation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 8, pp. 4225–4238, Aug. 2022.
[5]	Z. Cui, L. Gu, X. Sun, X. Ma, Y. Qiao, and T. Harada, “Aleth-NeRF: Illumination adaptive NeRF with concealing field assumption,” in Proc. 38th AAAI Conf. Artificial Intelligence, Vancouver, Canada, 2024, pp. 1435−1444.
[6]	K. A. Hashmi, G. Kallempudi, D. Stricker, and M. Z. Afzal, “FeatEnHancer: Enhancing hierarchical features for object detection and beyond under low-light vision,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Paris, France, 2023, pp. 6702−6712.
[7]	R. Liu, L. Ma, J. Zhang, X. Fan, and Z. Luo, “Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, USA, 2021, pp. 10561−10570.
[8]	H. Zhou, W. Dong, X. Liu, S. Liu, X. Min, G. Zhai, and J. Chen, “GLARE: Low light image enhancement via generative latent feature based codebook retrieval,” in Proc. 18th European Conf. Computer Vision, Milan, Italy, 2025, pp. 36−54.
[9]	R.-Q. Wu, Z.-P. Duan, C.-L. Guo, Z. Chai, and C. Li, “RIDCP: Revitalizing real image dehazing via high-quality codebook priors,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Vancouver, Canada, 2023, pp. 22282−22291.
[10]	X. Cong, J. Gui, J. Zhang, J. Hou, and H. Shen, “A semi-supervised nighttime dehazing baseline with spatial-frequency aware and realistic brightness constraint,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2024, pp. 2631−2640.
[11]	Y. Zhang, S. Zhou, and H. Li, “Depth information assisted collaborative mutual promotion network for single image dehazing,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2024, pp. 2846−2855.
[12]	X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable ConvNets V2: More deformable, better results,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, USA, 2019, pp. 9300−9308.
[13]	R. Girshick, “Fast R-CNN,” in Proc. IEEE Int. Conf. Computer Vision, Santiago, Chile, 2015, pp. 1440−1448.
[14]	S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, Jun. 2017. doi: 10.1109/TPAMI.2016.2577031
[15]	Z. Cai and N. Vasconcelos, “Cascade R-CNN: Delving into high quality object detection,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018, pp. 6154−6162.
[16]	H. Zhang, H. Chang, B. Ma, N. Wang, and X. Chen, “Dynamic R-CNN: Towards high quality object detection via dynamic training,” in Proc. 16th European Conf. Computer Vision, Glasgow, UK, 2020, pp. 260−275.
[17]	T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 318–327, Feb. 2020. doi: 10.1109/TPAMI.2018.2858826
[18]	Z. Tian, C. Shen, H. Chen, and T. He, “FCOS: Fully convolutional one-stage object detection,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Seoul, Korea (South), 2019, pp. 9626−9635.
[19]	Z. Tian, C. Shen, H. Chen, and T. He, “FCOS: A simple and strong anchor-free object detector,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 4, pp. 1922–1933, Apr. 2022. doi: 10.1109/tpami.2020.3032166
[20]	T. Kong, F. Sun, H. Liu, Y. Jiang, L. Li, and J. Shi, “FoveaBox: Beyound anchor-based object detection,” IEEE Trans. Image Process., vol. 29, pp. 7389–7398, Jun. 2020. doi: 10.1109/TIP.2020.3002345
[21]	S. Zhang, C. Chi, Y. Yao, Z. Lei, and S. Z. Li, “Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2020, pp. 9759−9768.
[22]	X. Dai, Y. Chen, B. Xiao, D. Chen, M. Liu, L. Yuan, and L. Zhang, “Dynamic head: Unifying object detection heads with attentions,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, USA, 2021, pp. 7373−7382.
[23]	J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, USA, 2016, pp. 779−788.
[24]	J. Redmon and A. Farhadi, “YOLO9000: Better, faster, stronger,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, USA, 2017, pp. 6517−6525.
[25]	R. Joseph and F. Ali, “YOLOv3: An incremental improvement,” arXiv preprint arXiv: 1804.02767, 2018.
[26]	A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv: 2004.10934, 2020.
[27]	C. Li, L. Li, H. Jiang, K. Weng, Y. Geng, L. Li, et al., “YOLOv6: A single-stage object detection framework for industrial applications,” arXiv preprint arXiv: 2209.02976, 2022.
[28]	C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Vancouver, Canada, 2023, pp. 7464−7475.
[29]	Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “YOLOX: Exceeding YOLO series in 2021,” arXiv preprint arXiv: 2107.08430, 2021.
[30]	Z.-X. Li, Y.-L. Wang, and F. Wang, “DI-YOLOv5: An improved dual-wavelet-based YOLOv5 for dense small object detection,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 2, pp. 457–459, Feb. 2025. doi: 10.1109/JAS.2024.124368
[31]	H. Sun, Y. Wang, J. Du, and R. Wang, “MFE-YOLO: A multi-feature fusion algorithm for airport bird detection,” ICCK Trans. Intell. Syst., vol. 2, no. 2, pp. 85–94, May 2025. doi: 10.62762/TIS.2025.323887
[32]	N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, “End-to-end object detection with transformers,” in Proc. 16th European Conf. Computer Vision, Glasgow, UK, 2020, pp. 213−229.
[33]	X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable DETR: Deformable transformers for end-to-end object detection,” in Proc. 9th Int. Conf. Learning Representations, Austria, 2021. [Online]. Available: https://iclr.cc/Conferences/2021
[34]	S. Liu, F. Li, H. Zhang, X. Yang, X. Qi, H. Su, J. Zhu, and L. Zhang, “DAB-DETR: Dynamic anchor boxes are better queries for DETR,” in Proc. 10th Int. Conf. Learning Representations, 2022. [Online]. Available: https://iclr.cc/Conferences/2022
[35]	F. Li, H. Zhang, S. Liu, J. Guo, L. M. Ni, and L. Zhang, “DN-DETR: Accelerate DETR training by introducing query DeNoising,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, New Orleans, USA, 2022, pp. 13609−13617.
[36]	F. Li, A. Zeng, S. Liu, H. Zhang, H. Li, L. Zhang, and L. M. Ni, “Lite DETR: An interleaved multi-scale encoder for efficient DETR,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Vancouver, Canada, 2023, pp. 18558−18567.
[37]	H. Zhang, F. Li, S. Liu, L. Zhang, H. Su, J. Zhu, L. M. Ni, and H.-Y. Shum, “DINO: DETR with improved DeNoising anchor boxes for end-to-end object detection,” in Proc. 11th Int. Conf. Learning Representations, Kigali, Rwanda, 2023.
[38]	Z. Zong, G. Song, and Y. Liu, “DETRs with collaborative hybrid assignments training,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Paris, France, 2023, pp. 6725−6735.
[39]	X. Hou, M. Liu, S. Zhang, P. Wei, and B. Chen, “Salience DETR: Enhancing detection transformer with hierarchical salience filtering refinement,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2024, pp. 17574−17583.
[40]	X. Hou, M. Liu, S. Zhang, P. Wei, B. Chen, and X. Lan, “Relation DETR: Exploring explicit position relation prior for object detection,” in Proc. 18th European Conf. Computer Vision, Milan, Italy, 2025, pp. 89−105.
[41]	Y. Zhang, J. Zhang, and X. Guo, “Kindling the darkness: A practical low-light image enhancer,” in Proc. 27th ACM Int. Conf. Multimedia, Nice, France, 2019, pp. 1632−1640.
[42]	C. Guo, C. Li, J. Guo, C. C. Loy, J. Hou, S. Kwong, and R. Cong, “Zero-reference deep curve estimation for low-light image enhancement,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2020, pp. 1777−1786.
[43]	Y. Jiang, X. Gong, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang, “EnlightenGAN: Deep light enhancement without paired supervision,” IEEE Trans. Image Process., vol. 30, pp. 2340–2349, Jan. 2021. doi: 10.1109/TIP.2021.3051462
[44]	F. Lv, Y. Li, and F. Lu, “Attention guided low-light image enhancement with a large scale low-light simulation dataset,” Int. J. Comput. Vis., vol. 129, no. 7, pp. 2175–2193, Jul. 2021. doi: 10.1007/s11263-021-01466-8
[45]	W. Liu, G. Ren, R. Yu, S. Guo, J. Zhu, and L. Zhang, “Image-adaptive YOLO for object detection in adverse weather conditions,” in Proc. 36th AAAI Conf. Artificial Intelligence, 2022, pp. 1792−1800. [Online]. Available: https://aaai-2022.virtualchair.net/venue
[46]	Z. Cui, G.-J. Qi, L. Gu, S. You, Z. Zhang, and T. Harada, “Multitask AET with orthogonal tangent regularity for dark object detection,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Montreal, Canada, 2021, pp. 2533−2542.
[47]	J. Wang, Y. He, K. Li, S. Li, L. Zhao, J. Yin, M. Zhang, T. Shi, and X. Wang, “MDANet: A multi-stage domain adaptation framework for generalizable low-light image enhancement,” Neurocomputing, vol. 627, Art. no. 129572, Apr. 2025. doi: 10.1016/j.neucom.2025.129572
[48]	H. Dong, J. Pan, L. Xiang, Z. Hu, X. Zhang, F. Wang, and M.-H. Yang, “Multi-scale boosted dehazing network with dense feature fusion,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2020, pp. 2154−2164.
[49]	C. Guo, Q. Yan, S. Anwar, R. Cong, W. Ren, and C. Li, “Image dehazing transformer with transmission-aware 3D position embedding,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, New Orleans, USA, 2022, pp. 5802−5810.
[50]	Y. Shao, L. Li, W. Ren, C. Gao, and N. Sang, “Domain adaptation for image dehazing,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2020, pp. 2805−2814.
[51]	Z. Chen, Y. Wang, Y. Yang, and D. Liu, “PSD: Principled synthetic-to-real dehazing guided by physical priors,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, USA, 2021, pp. 7180−7189.
[52]	Y. Yang, C. Wang, R. Liu, L. Zhang, X. Guo, and D. Tao, “Self-augmented unpaired image dehazing via density and depth decomposition,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, New Orleans, SUA, 2022, pp. 2027−2036.
[53]	T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, USA, 2017, pp. 936−944.
[54]	S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018, pp. 8759−8768.
[55]	Q. Zhao, T. Sheng, Y. Wang, Z. Tang, Y. Chen, L. Cai, and H. Ling, “M2Det: A single-shot object detector based on multi-level feature pyramid network,” in Proc. 33rd AAAI Conf. Artificial Intelligence, Honolulu, USA, 2019, pp. 9259−9266.
[56]	F. Wang and S. Yi, “Spatio-temporal feature soft correlation concatenation aggregation structure for video action recognition networks,” ICCK Trans. Sens. Commun. Control, vol. 1, no. 1, pp. 60–71, Oct. 2024. doi: 10.62762/TSCC.2024.212751
[57]	S. Rafique, S. Iqbal, D. Ali, and F. Khan, “Navigating ethical challenges in 6G-enabled smart cities: Privacy, equity, and governance,” ICCK Trans. Sens. Commun. Control, vol. 2, no. 1, pp. 48–65, Mar. 2025. doi: 10.62762/TSCC.2025.291581
[58]	E. Mohammed, A. Khan, W. Ullah, W. Khan, and M. J. Ahmed, “Efficient polyp segmentation via attention-guided lightweight network with progressive multi-scale fusion,” ICCK Trans. Intell. Syst., vol. 2, no. 2, pp. 95–108, Jun. 2025. doi: 10.62762/TIS.2025.389995
[59]	J. Hua, Z. Wang, J. Chang, H. Zou, G. Wu, and J. Ma, “MSTDNet: Multi-scale traffic object detection network with smooth information perception,” Pattern Recognit., vol. 172, Art. no. 112579, Apr. 2026. doi: 10.1016/j.patcog.2025.112579
[60]	Q. Chen, Y. Wang, T. Yang, X. Zhang, J. Cheng, and J. Sun, “You only look one-level feature,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, USA, 2021, pp. 13039−13048.
[61]	G. Yang, J. Lei, Z. Zhu, S. Cheng, Z. Feng, and R. Liang, “AFPN: Asymptotic feature pyramid network for object detection,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, Honolulu, USA, 2023, pp. 2184−2189.
[62]	L. Zhu, Z. Deng, X. Hu, C.-W. Fu, X. Xu, J. Qin, and P.-A. Heng, “Bidirectional feature pyramid network with recurrent attention residual modules for shadow detection,” in Proc. 15th European Conf. Computer Vision, Munich, Germany, 2018, pp. 122−137.
[63]	P.-Y. Chen, M.-C. Chang, J.-W. Hsieh, and Y.-S. Chen, “Parallel residual bi-fusion feature pyramid network for accurate single-shot object detection,” IEEE Trans. Image Process., vol. 30, pp. 9099–9111, Nov. 2021. doi: 10.1109/TIP.2021.3118953
[64]	C. Wang, W. He, Y. Nie, J. Guo, C. Liu, K. Han, and Y. Wang, “Gold-YOLO: Efficient object detector via gather-and-distribute mechanism,” in Proc. 37th Int. Conf. Neural Information Processing Systems, New Orleans, USA, 2023, Art. no. 2224.
[65]	X. Ding, X. Zhang, J. Han, and G. Ding, “Scaling up your kernels to 31×31: Revisiting large kernel design in CNNs,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, New Orleans, USA, 2022, pp. 11953−11965.
[66]	S. Lin, F. Tang, W. Dong, X. Pan, and C. Xu, “SMNet: Synchronous multi-scale low light enhancement network with local and global concern,” IEEE Trans. Multimedia, vol. 25, pp. 9506–9517, Mar. 2023. doi: 10.1109/TMM.2023.3254141
[67]	Y. Qi, Y. He, X. Qi, Y. Zhang, and G. Yang, “Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Paris, France, 2023, pp. 6047−6056.
[68]	Y. P. Loh and C. S. Chan, “Getting to know low-light images with the exclusively dark dataset,” Comput. Vis. Image Underst., vol. 178, pp. 30–42, Jan. 2019. doi: 10.1016/j.cviu.2018.10.010
[69]	W. Yang, Y. Yuan, W. Ren, J. Liu, W. J. Scheirer, Z. Wang, et al, “Advancing image understanding in poor visibility environments: A collective benchmark study,” IEEE Trans. Image Process., vol. 29, pp. 5737–5752, Mar. 2020. doi: 10.1109/TIP.2020.2981922
[70]	C. Sakaridis, D. Dai, S. Hecker, and L. Van Gool, “Model adaptation with synthetic and real data for semantic dense foggy scene understanding,” in Proc. 15th European Conf. Computer Vision, Munich, Germany, 2018, pp. 707−724.
[71]	C. Feng, Z. Chen, X. Li, C. Wang, J. Yang, M.-M. Cheng, Y. Dai, and Q. Fu, “HazyDet: Open-source benchmark for drone-view object detection with depth-cues in hazy scenes,” arXiv preprint arXiv: 2409.19833, 2025.
[72]	T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in Proc. 13th European Conf. Computer Vision, Zurich, Switzerland, 2014, pp. 740−755.
[73]	N. Wang, Y. Gao, H. Chen, P. Wang, Z. Tian, C. Shen, and Y. Zhang, “NAS-FCOS: Fast neural architecture search for object detection,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2020, pp. 11940−11948.
[74]	H. Zhang, Y. Wang, F. Dayoub, and N. Sünderhauf, “VarifocalNet: An IoU-aware dense object detector,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Nashville, USA, 2021, pp. 8514−8523.
[75]	D. Meng, X. Chen, Z. Fan, G. Zeng, H. Li, Y. Yuan, L. Sun, and J. Wang, “Conditional DETR for fast training convergence,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Montreal, Canada, 2021, pp. 3631−3640.
[76]	X. Li, C. Lv, W. Wang, G. Li, L. Yang, and J. Yang, “Generalized focal loss: Towards efficient representation learning for dense object detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 3, pp. 3139–3153, Mar. 2023. doi: 10.1109/tpami.2022.3180392
[77]	P. Sun, R. Zhang, Y. Jiang, T. Kong, C. Xu, W. Zhan, M. Tomizuka, Z. Yuan, and P. Luo, “Sparse R-CNN: An end-to-end framework for object detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 12, pp. 15650–15664, Dec. 2023. doi: 10.1109/TPAMI.2023.3292030
[78]	S. Zhang, X. Wang, J. Wang, J. Pang, C. Lyu, W. Zhang, P. Luo, and K. Chen, “Dense distinct query for end-to-end object detection,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Vancouver, Canada, 2023, pp. 7329−7338.
[79]	X. Liu, Y. Ma, Z. Shi, and J. Chen, “GridDehazeNet: Attention-based multi-scale network for image dehazing,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Seoul, Korea (South), 2019, pp. 7314−7323.
[80]	L. Lu, Q. Xiong, B. Xu, and D. Chu, “MixDehazeNet: Mix structure block for image dehazing network,” in Proc. Int. Joint Conf. Neural Networks, Yokohama, Japan, 2024, pp. 1−10.
[81]	Y. Cui and A. Knoll, “Dual-domain strip attention for image restoration,” Neural Networks, vol. 171, pp. 429–439, Mar. 2024. doi: 10.1016/j.neunet.2023.12.003
[82]	X. Qin, Z. Wang, Y. Bai, X. Xie, and H. Jia, “FFA-Net: Feature fusion attention network for single image dehazing,” in Proc. 34th AAAI Conf. Artificial Intelligence, New York, USA, 2020, pp. 11908−11915.
[83]	Y. Song, Z. He, H. Qian, and X. Du, “Vision transformers for single image dehazing,” IEEE Trans. Image Process., vol. 32, pp. 1927–1941, Mar. 2023. doi: 10.1109/TIP.2023.3256763
[84]	Y. Song, Y. Zhou, H. Qian, and X. Du, “Rethinking performance gains in image dehazing networks,” arXiv preprint arXiv: 2209.11448, 2022.
[85]	Y. Zheng, J. Zhan, S. He, J. Dong, and Y. Du, “Curricular contrastive regularization for physics-aware single image dehazing,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Vancouver, Canada, 2023, pp. 5785−5794.
[86]	K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341–2353, Dec. 2011. doi: 10.1109/TPAMI.2010.168
[87]	C. Guo, B. Fan, Q. Zhang, S. Xiang, and C. Pan, “AugFPN: Improving multi-scale feature learning for object detection,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Seattle, USA, 2020, pp. 12592−12601.
[88]	D. Zhang, H. Zhang, J. Tang, M. Wang, X. Hua, and Q. Sun, “Feature pyramid transformer,” in Proc. 16th European Conf. Computer Vision, Glasgow, UK, 2020, pp. 323−339.
[89]	J. Ma and B. Chen, “Dual refinement feature pyramid networks for object detection,” arXiv preprint arXiv: 2012.01733, 2020.
[90]	G. Zhao, W. Ge, and Y. Yu, “GraphFPN: Graph feature pyramid network for object detection,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Montreal, Canada, 2021, pp. 2743−2752.
[91]	J. Xie, Y. Pang, J. Nie, J. Cao, and J. Han, “Latent feature pyramid network for object detection,” IEEE Trans. Multimedia, vol. 25, pp. 2153–2163, Jan. 2023. doi: 10.1109/TMM.2022.3143707
[92]	L. Zhu, F. Lee, J. Cai, H. Yu, and Q. Chen, “An improved feature pyramid network for object detection,” Neurocomputing, vol. 483, pp. 127–139, Apr. 2022. doi: 10.1016/j.neucom.2022.02.016
[93]	K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in Proc. IEEE Int. Conf. Computer Vision, Venice, Italy, 2017, pp. 2980−2988.

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Robust Information Feature Pyramid Networks for Low-Visible Object Detection

doi: 10.1109/JAS.2026.126065

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