[1] Iftikhar S, Asim M, Zhang Z P et al. Target detection and recognition for traffic congestion in smart cities using deep learning-enabled UAVs: a review and analysis[J]. Applied Sciences, 13, 3995(2023).

[2] Li X, He M, Luo H B. Occluded pedestrian detection algorithm based on improved YOLOv3[J]. Acta Optica Sinica, 42, 1415003(2022).

[3] Jiang Z T, Xiao Y, Zhang S Q et al. Low-illumination object detection method based on dark-YOLO[J]. Journal of Computer-Aided Design & Computer Graphics, 35, 441-451(2023).

[4] Wei H T, Yu B, Wang W et al. Adaptive enhanced detection network for low illumination object detection[J]. Mathematics, 11, 2404(2023).

[5] Cui Z T, Li K C, Gu L et al. You only need 90 K parameters to adapt light: a light weight transformer for image enhancement and exposure correction[C], 238(2022).

[6] Ren K, Tao Q Y, Han H G. A lightweight object detection network in low-light conditions based on depthwise separable pyramid network and attention mechanism on embedded platforms[J]. Journal of the Franklin Institute, 360, 4427-4455(2023).

[7] Wei C, Wang W J, Yang W H et al. Deep retinex decomposition for low-light enhancement[C], 155(2018).

[8] Jiang Y F, Gong X Y, Liu D et al. EnlightenGAN: deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing, 30, 2340-2349(2021).

[9] Sasagawa Y, Nagahara H, Vedaldi A, Bischof H, Brox T et al. YOLO in the dark-domain adaptation method for merging multiple models[M]. Computer vision-ECCV 2020. Lecture notes in computer science, 12366, 345-359(2020).

[10] Cui Z T, Qi G J, Gu L et al. Multitask AET with orthogonal tangent regularity for dark object detection[C], 2533-2542(2021).

[11] Underwood E. How humans: and other mammals: might have gotten their night vision[J]. Science, eaav4172(2018).

[12] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C], 7464-7475(2023).

[13] Ma H Q, Ma S P, Xu Y L et al. Low-light image enhancement based on deep convolutional neural network[J]. Acta Optica Sinica, 39, 0210004(2019).

[14] Zhang Y H, Zhang J W, Guo X J. Kindling the darkness: a practical low-light image enhancer[C], 1632-1640(2019).

[15] Wang Y F, Wan R J, Yang W H et al. Low-light image enhancement with normalizing flow[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 2604-2612(2022).

[16] Fan C M, Liu T J, Liu K H. Half wavelet attention on M-net for low-light image enhancement[C], 3878-3882(2022).

[17] Stergiou A, Poppe R, Kalliatakis G. Refining activation downsampling with SoftPool[C], 10337-10346(2021).

[18] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C], 3431-3440(2015).

[19] Wang Y, Gong X J, Cheng J. Metal workpiece surface defect segmentation method based on improved U-net[J]. Laser & Optoelectronics Progress, 60, 1524001(2023).

[20] Ramachandran P, Zoph B, Le Q V. Searching for activation functions[C](2018).

[21] Jocher G, Nishimura K, Mineeva T et al. Yolov5[EB/OL]. https://github.com/ultralytics/yolov5

[22] Li C Y, Li L L, Jiang H L et al. YOLOv6: a single-stage object detection framework for industrial applications[EB/OL]. http://arxiv.org/abs/2209.02976

[23] Lü C, Zhang W, Huang H et al. Rtmdet: an empirical study of designing real-time object detectors[EB/OL]. https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet

[24] Woo S, Park J, Lee J Y, Ferrari V, Hebert M, Sminchisescu C et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[25] Loh Y P, Chan C S. Getting to know low-light images with the exclusively dark dataset[J]. Computer Vision and Image Understanding, 178, 30-42(2019).

[26] Lin T Y, Maire M, Belongie S, Fleet D, Pajdla T, Schiele B et al. Microsoft COCO: common objects in context[M]. Computer vision-ECCV 2014. Lecture notes in computer science, 8693, 740-755(2014).

[27] Jocher G. YOLOv8[EB/OL]. https://github.com/ultralytics/ultralytics

[28] Jiang Z T, Zhai F S, Qian Y et al. Low illumination object detection combined with feature enhancement and multi-scale receptive field[J]. Journal of Computer Research and Development, 60, 903-915(2023).

[29] Shu Z T, Zhang Z B, Song Y Z et al. Low-light image object detection based on improved YOLOv5 algorithm[J]. Laser & Optoelectronics Progress, 60, 0404001(2023).