[1] [1] A HASELHOFF, A KUMMERT. A vehicle detection system based on Haar and triangle features ［C］. 2009 IEEE Intelligent Vehicles Symposium, 2009: 261-266.

[2] [2] LIU F, WANG S B, WANG X J, et al.. Infrared pedestrian detection method in low visibility environment based on multi feature association ［J］. Infrared and Laser Engineering, 2018, 47(6): 127-134. (in Chinese)

[3] [3] J REDMON, A FARHADI. YOLO9000: Better, faster, stronger［C］. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017: 6517-6525.

[4] [4] S REN, K HE, R GIRSHICK. Faster R-CNN: Towards real-time object detection with region proposal networks ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

[5] [5] FAN L L, ZHAO H W, ZHAO H Y, .et al.. Survey of target detection based on deep convolutional neural networks［J］. Opt. Precision Eng., 2020, 28(5): 1152-1164. (in Chinese)

[6] [6] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4): 640-651.

[7] [7] REN F L, HE X, WEI ZH H, et al.. Semantic segmentation based on DeepLabV3+ and superpixel optimization［J］. Opt. Precision Eng., 2019, 27(12): 2722-2729. (in Chinese)

[8] [8] WANG ZH Y, NI X Y, SHANG ZH D. Autonomous driving semantic segmentation with convolution neural networks［J］. Opt. Precision Eng., 2019, 27(11): 2429-2438. (in Chinese)

[9] [9] KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet classification with deep convolutional neural networks［J］. Advances in neural information processing systems, 2012, 25(2): 1097-1105.

[10] [10] TAN M, LE Q V. EfficientNet: Rethinking model scaling for convolutional neural networks［C］. 36th International Conference on Machine Learning (ICML), California, 2019: 691-700.

[11] [11] GIUSTI A, GUZZI J, CIRESAN D, et al.. A machine learning approach to visual perception of forest trails for mobile robots［J］. IEEE Robotics & Automation Letters, 2016, 1(2): 661-667.

[12] [12] C RICHTER, N ROY. Safe visual navigation via deep learning and novelty detection［C］. Robotics: Science and Systems XIII (RSS), Massachussets, 2017: 64-73.

[13] [13] LIU Y J, ZHU SH P. Image enhancement algorithm based on global and local multi features［J］. Chinese Journal of Liquid Crystals and Displays, 2020, 35(5): 508-512.

[14] [14] K HE, X ZHANG, S REN, et al.. Deep residual learning for image recognition［C］. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016: 770-778.

[15] [15] IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift［C］. Proceedings of the 32nd International Conference on Machine Learning, Lille, 2015: 448-456.

[16] [16] LOQUERCIO A, SEG? M, SCARAMUZZA D. A general framework for uncertainty estimation in deep learning［J］. IEEE Robotics and Automation Letters, 2019, 5: 3153-3160.

[17] [17] LOQUERCIO A, MAQUEDA A I, DEL-BLANCO C R, et al.. DroNet: learning to fly by driving［J］. IEEE Robotics & Automation Letters, 2018, 3(2): 1088-1095.

[18] [18] K SIMONYAN, A ZISSERMAN. Very deep convolutional networks for large-scale image recognition［C］. International Conference on Learning Representations (ICLR), San Diego, 2015: 1-14.

[19] [19] C SZEGEDY, V VANHOUCKE, S IOFFE, et al.. Rethinking the inception architecture for computer vision［C］. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016: 2818-2826.

[20] [20] M SANDLER, A HOWARD, M ZHU, et al.. MobileNetV2: Inverted residuals and linear bottlenecks［C］. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, 2018: 4510-4520.

[21] [21] A HOWARD, M SANDLER, G CHU, et al.. Searching for mobileNetV3［C］. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019: 1314-1324.

[22] [22] B ZHOU, A KHOSLA, A LAPEDRIZA, et al.. Learning deep features for discriminative localization［C］. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016: 2921-2929.