[1] ZHOU Y, TUZEL O. Voxelnet: end-to-end learning for point cloud based 3d object detection[C], 4490-4499(2018).

[2] KIM S, SONG W J, KIM S H. Infrared variation optimized deep convolutional neural network for robust automatic ground target recognition[C], 1-8(2017).

[3] LIU Yuhang, HUANG Zhenghua, SONG Qiong et al. PV-YOLO: a lightweight pedestrian and vehicle detection model based on improved YOLOv8[J]. Digital Signal Processing, 156, 104857-104857(2025).

[4] SHI Huimin. Research on lightweight real-time target detection method based on attention mechanism[D](2023).

[5] GIRSHICK R. Fast R-CNN[C], 1440-1448(2015).

[6] REN S, HE K, GIRSHICK R. Faster R-CNN: towards real-time object detection with region proposal networks[J]. Advances in Neural Information Processing Systems, 28, 91-99(2015).

[7] LIU W, ANGUELOV D, ERHAN D. SSD: single shot multibox detector[C], 21-37(2016).

[8] REDMON J, DIVVALA S, GIRSHICK R. You only look once: unified, real-time object detection[C], 779-788(2016).

[9] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C], 7263-7271(2017).

[10] REDMON J, FARHADI A. Yolov3: an incremental improvement[J/OL]. https://arxiv.org/abs/1804.02767

[11] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. Yolov4: optimal speed and accuracy of object detection[J/OL]. https://arxiv.org/abs/2004.10934

[12] LI Chongyi, GUO Chunle, HAN Linghao et al. Low-light image and video enhancement using deep learning: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 9396-9416(2021).

[13] LORE G K, AKINTAYO A, SARKAR S. LLNet: a deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition, 61, 650-662(2017).

[14] CHEN Zilong, LIANG Yaling, DU Minghui. Attention-based broad self-guided network for low-light image enhancement[C], 31-38(2022).

[15] WU Wenhui, WENG Jian, ZHANG Pingping et al. URetinex-Net: retinex-based deep unfolding network for low-light image enhancemen[C], 5901-5910(2022).

[16] XU Dan, OUYANG W, RICCI E. Learning cross-modal deep representations for robust pedestrian detection[C], 5363-5371(2017).

[17] PARK K, KIM S, SOHN K. Unified multi-spectral pedestrian detection based on probabilistic fusion networks[J]. Pattern Recognition, 80, 143-155(2018).

[18] DAI Xuerui, YUAN Xue, WEI Xueye. TIRNet: object detection in thermal infrared images for autonomous driving[J]. Applied Intelligence, 51, 1-18(2020).

[19] CAO Yanpeng, LUO Xing, YANG Jiangxin et al. Locality guided cross-modal feature aggregation and pixel-level fusion for multispectral pedestrian detection[J]. Information Fusion, 88, 1-11(2022).

[20] HWANG S, PARK J, KIM N et al. Multispectral pedestrian detection: benchmark dataset and baseline[C], 1037-1045(2015).

[21] PIOTR D, RON A, SERGE B et al. Fast feature pyramids for object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1532-1545(2014).

[22] FANG Qingyun, HAN Dapeng, WANG Zhaokui. Cross-modality fusion transformer for multispectral object detection[J/OL]. https://arxiv.org/abs/2111.00273

[23] LI Chengyang, SONG Dan, TONG Ruofeng et al. Illumination-aware faster R-CNN for robust multispectral pedestrian detection[J]. Pattern Recognition, 85, 161-171(2019).

[24] SUN Ying, HOU Zhiqiang, YANG Chen et al. Object detection algorithm based on dual-modal fusion network[J]. Acta Photonica Sinica, 52, 0110002(2023).

[25] YANG Chen, HOU Zhiqiang, LI Xinyue et al. Object detection algorithm based on CNN-transformer dualmodal feature fusion[J]. Acta Photonica Sinica, 53, 0310001(2024).

[26] HOU Zhiqiang, LI Xinyue, YANG Chen et al. Dual-branch network object detection algorithm based on dual-modality fusion of visible and infrared images[J]. Multimedia Systems, 30, 333(2024).

[27] CHEN Yunfan, XIE Han, SHIN H. Multi-layer fusion techniques using a CNN for multispectral pedestrian detection[J]. IET Computer Vision, 12, 1179-1187(2018).

[28] ZHUANG Yifan, PU Ziyuan, HU Jia et al. Illumination and temperature-aware multispectral networks for edge-computing-enabled pedestrian detection[J]. IEEE Transactions on Network Science and Engineering, 9, 1282-1295(2022).

[29] GUAN Dayan, LUO Xing, CAO Yanpeng et al. Unsupervised domain adaptation for multispectral pedestrian detection[C], 434-443(2019).

[30] ZHOU Kailai, CHEN Linsen, CAO Xun. Improving multispectral pedestrian detection by addressing modality imbalance problems[C], 787-803(2020).

[31] LIU J, ZHANG S, WANG S et al. Multispectral deep neural networks for pedestrian detection[J/OL]. https://arxiv.org/abs/1611.02644

[32] LI C, SONG D, TONG R. Multispectral pedestrian detection via simultaneous detection and segmentation[J/OL]. https://arxiv.org/abs/1808.04818

[33] ZHANG Xue, ZHANG Xiaohan, WANG Jiangcheng et al. TFDet: target-aware fusion for RGB-T pedestrian detection[J]. IEEE Transactions on Neural Networks and Learning Systems, 1-15(2024).

[34] SHEN Jifeng, CHEN Yifei, LIU Yue et al. ICAFusion: iterative cross-attention guided feature fusion for multispectral object detection[J]. Pattern Recognition, 145, 109913(2024).

[35] JI Qingbo, QI Yufei. Dual-mode object detection algorithm based on feature enhancement and feature fusion[J]. Journal of Physics: Conference Series, 2816, 012091-012091(2024).

[36] HOU Zhiqiang, YANG Chen, SUN Ying et al. An object detection algorithm based on infrared-visible dual modal feature fusion[J]. Infrared Physics and Technology, 137, 105107(2024).

[37] SANDLER M, HOWARD A, ZHU Menglong et al. Mobilenetv2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).

[38] LI C, SONG D, TONG R et al. Multispectral pedestrian detection via simultaneous detection and segmentation[J/OL]. https://arxiv.org/abs/1611.02644

[39] GAO Qi, ZHANG Cong, SHI Rui et al. An unmanned aircraft target detection method based on cross-modal progressive fusion[J]. Unmanned Systems Technology, 7, 54-64(2024).