[1] [1] 1陈莹， 朱宇. 模态自适应权值学习机制下的多光谱行人检测网络［J］. 光学 精密工程， 2020， 28（12）： 2700-2709. doi: 10.37188/OPE.20202812.2700CHENY， ZHUY. Multispectral pedestrian detection network under modal adaptive weight learning mechanism［J］. Optics and Precision Engineering， 2020， 28（12）： 2700-2709.（in Chinese）. doi: 10.37188/OPE.20202812.2700

[2] [2] 2杨琦， 车进， 张良， 等. GAN网络混合编码的行人再识别［J］. 液晶与显示， 2021， 36（2）： 334-342. doi: 10.37188/CJLCD.2020-0167YANGQ， CHEJ， ZHANGL， et al. Person re-identification of GAN network hybrid coding［J］. Chinese Journal of Liquid Crystals and Displays， 2021， 36（2）： 334-342.（in Chinese）. doi: 10.37188/CJLCD.2020-0167

[3] LI T, MA Y T, SHEN H et al. FPGA implementation of real-time pedestrian detection using normalization-based validation of adaptive features clustering[J]. IEEE Transactions on Vehicular Technology, 69, 9330-9341(2020).

[4] KANAZAWA A, BLACK M J, JACOBS D W et al. End-to-end recovery of human shape and pose[C], 7122-7131(2018).

[5] [5] 5穆靖， 李伟华， 饶俊民， 等. 采用三层模板局部差异度量的红外弱小目标检测［J］. 光学 精密工程， 2022， 30（7）： 869-882. doi: 10.37188/OPE.20223007.0869MUJ， LIW H， RAOJ M， et al. Infrared small target detection using tri-layer template local difference measure［J］. Optics and Precision Engineering， 2022， 30（7）： 869-882.（in Chinese）. doi: 10.37188/OPE.20223007.0869

[6] BISWAS S K, MILANFAR P. Linear support tensor machine with LSK channels： pedestrian detection in thermal infrared images[J]. IEEE Transactions on Image Processing, 26, 4229-4242(2017).

[7] REN S Q, HE K M, GIRSHICK R et al. Faster R-CNN： towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[8] [8] 8刘琼， 罗晴， 彭绍武. 车载热成像行人检测RoI提取方法［J］. 东北大学学报（自然科学版）， 2020， 41（8）： 1083-1090. doi: 10.12068/j.issn.1005-3026.2020.08.004LIUQ， LUOQ， PENGSH W. RoI extraction for vehicular thermal infrared pedestrian detection［J］. Journal of Northeastern University （Natural Science）， 2020， 41（8）： 1083-1090.（in Chinese）. doi: 10.12068/j.issn.1005-3026.2020.08.004

[9] CHEN Y Y, JHONG S Y, LI G Y et al. Thermal-based pedestrian detection using faster R-CNN and region decomposition branch[C], 1-2(2019).

[10] XU Z W, VONG C M, WONG C C et al. Ground plane context aggregation network for day-and-night on vehicular pedestrian detection[J]. IEEE Transactions on Intelligent Transportation Systems, 22, 6395-6406(2021).

[11] PEI D S, JING M X, LIU H P et al. A fast RetinaNet fusion framework for multi-spectral pedestrian detection[J]. Infrared Physics & Technology, 105, 103178(2020).

[12] WANG D, LAN J H. PPDet： a novel infrared pedestrian detection network in a per-pixel prediction fashion[J]. Infrared Physics & Technology, 119, 103965(2021).

[13] [13] 13李经宇， 杨静， 孔斌， 等. 基于注意力机制的多尺度车辆行人检测算法［J］. 光学 精密工程， 2021， 29（6）： 1448-1458. doi: 10.37188/OPE.20212906.1448LIJ Y， YANGJ， KONGB， et al. Multi-scale vehicle and pedestrian detection algorithm based on attention mechanism［J］. Optics and Precision Engineering， 2021， 29（6）： 1448-1458.（in Chinese）. doi: 10.37188/OPE.20212906.1448

[14] [14] 14刘怡帆， 王旭飞， 周鹏， 等. 基于YOLOv4神经网络的红外图像道路行人检测［J］. 数字技术与应用， 2021， 39（10）： 19-22. doi: 10.19695/j.cnki.cn12-1369.2021.10.07LIUY F， WANGX F， ZHOUP， et al. Road pedestrian detection based on YOLOv4 neural network in infrared image［J］. Digital Technology ＆ Application， 2021， 39（10）： 19-22.（in Chinese）. doi: 10.19695/j.cnki.cn12-1369.2021.10.07

[15] LIU Y, SU H L, ZENG C et al. A robust thermal infrared vehicle and pedestrian detection method in complex scenes[J]. Sensors （Basel， Switzerland）, 21, 1240(2021).

[16] ZHENG Z H, WANG P, REN D W et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 52, 8574-8586(2022).

[17] [17] 17贺愉婷， 车进， 吴金蔓. 基于YOLOv5和重识别的行人多目标跟踪方法［J］. 液晶与显示， 2022， 37（7）： 880-890. doi: 10.37188/CJLCD.2022-0025HEY T， CHEJ， WUJ M. Pedestrian multi-target tracking method based on YOLOv5 and person re-identification［J］. Chinese Journal of Liquid Crystals and Displays， 2022， 37（7）： 880-890.（in Chinese）. doi: 10.37188/CJLCD.2022-0025

[18] FAN H Y, MA J S, FAN H H et al. Iterative quadtree decomposition based automatic selection of the seed point for ultrasound breast tumor images[J]. Multimedia Tools and Applications, 76, 3505-3517(2017).

[19] JANG Y et al. Edge-enhancing bi-histogram equalisation using guided image filter[J]. Journal of Visual Communication and Image Representation, 58, 688-700(2019).

[20] MA J Y, MA Y, LI C. Infrared and visible image fusion methods and applications： a survey[J]. Information Fusion, 45, 153-178(2019).

[21] EMARY E, ZAWBAA H M, GROSAN C. Experienced gray wolf optimization through reinforcement learning and neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 29, 681-694(2018).

[22] BASHI-AZGHADI S N, AFSHAR A, AFSHAR M H. Multi-period response management to contaminated water distribution networks： dynamic programming versus genetic algorithms[J]. Engineering Optimization, 50, 415-429(2018).

[23] KHISHE M, MOSAVI M R. Chimp optimization algorithm[J]. Expert Systems With Applications, 149, 113338(2020).

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

[25] [25] 25王灿芳， 崔良玉， 阎兵. 基于Variance-Brenner函数的显微图像清晰度评价算法研究［J］. 装备制造技术， 2020（10）： 78-82. doi: 10.3969/j.issn.1672-545X.2020.10.020WANGC F， CUIL Y， YANB. Study on the microscopic image sharpness evaluation algorithm based on variance-Brenner function［J］. Equipment Manufacturing Technology， 2020（10）： 78-82.（in Chinese）. doi: 10.3969/j.issn.1672-545X.2020.10.020

[26] [26] 26孙红利， 冯旗， 董峰. 图像清晰度评价算法研究［J］. 传感器与微系统， 2017， 36（2）： 67-70. doi: 10.13873/J.1000-9787(2017)02-0067-04SUNH L， FENGQ， DONGF. Research on algorithm for image clarity evaluation［J］. Transducer and Microsystem Technologies， 2017， 36（2）： 67-70.（in Chinese）. doi: 10.13873/J.1000-9787(2017)02-0067-04

[27] LIU W, ANGUELOV D, ERHAN D et al. SSD： Single Shot MultiBox Detector[M]. Computer Vision - ECCV 2016, 21-37(2016).

[28] LIN T Y, GOYAL P, GIRSHICK R et al. Focal loss for dense object detection[C], 2999-3007(2017).

[29] SUN P Z, ZHANG R F, JIANG Y et al. Sparse R-CNN： end-to-end object detection with learnable proposals[C], 14449-14458(2021).

[30] ZHANG H Y, WANG Y, DAYOUB F et al. VarifocalNet： an IoU-aware dense object detector[C], 8510-8519(2021).

[31] FENG C J, ZHONG Y J, GAO Y et al. TOOD： task-aligned one-stage object detection[C], 3490-3499(2021).

[32] SUN M Y, ZHANG H C, HUANG Z L et al. Road infrared target detection with I‐YOLO[J]. IET Image Processing, 16, 92-101(2022).

[33] Kieu M, Bagdanov A D, Bertini M et al. Task-conditioned domain adaptation for pedestrian detection in thermal imagery[C], 546-562(2020).