[1] 夏景明, 徐子峰, 谈玲. 轻量化网络LW-GCNet在垃圾分类中的应用[J]. 环境工程, 2023, 41(2): 173-180.
[2] 赵冬娥, 吴瑞, 赵宝国, 等. 高光谱成像的垃圾分类识别研究[J]. 光谱学与光谱分析, 2019, 39(3): 921-926.
[3] XIAO W, YANG J H, FANG H Y, et al. A robust classification algorithm for separation of construction waste using NIR hyperspectral system[J]. Waste Management, 2019, 90: 1-9. doi: 10.1016/j.wasman.2019.04.036
[4] 赵珊, 刘子路, 郑爱玲, 等. 基于MobileNetV2和IFPN改进的SSD垃圾实时分类检测方法[J]. 计算机应用, 2022, 42(S1): 106-111.
[5] ZHANG Q, ZHANG X, MU X, et al. Recyclable waste image recognition based on deep learning[J]. Resources, Conservation and Recycling, 2021, 171(99): 105636.
[6] ZHANG S, CHEN Y M, YANG Z L, et al. Computer vision based two-stage waste recognition-retrieval algorithm for waste classification[J]. Resources, Conservation and Recycling, 2021, 169: 105543. doi: 10.1016/j.resconrec.2021.105543
[7] 高明, 陈玉涵, 张泽慧, 等. 基于新型空间注意力机制和迁移学习的垃圾图像分类算法[J]. 系统工程理论与实践, 2021, 41(2): 498-512.
[8] 康庄, 杨杰, 郭濠奇. 基于机器视觉的垃圾自动分类系统设计[J]. 浙江大学学报(工学版), 2020, 54(7): 1272-1280+1307.
[9] 马雯, 于炯, 王潇, 等. 基于改进Faster R-CNN的垃圾检测与分类方法[J]. 计算机工程, 2021, 47(8): 294-300.
[10] LIN K S, ZHOU T, GAO X F, et al. Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer[J]. Journal of Environmental Management, 2022, 318: 115501. doi: 10.1016/j.jenvman.2022.115501
[11] NOWAKOWSKI P, PAMUA T. Application of deep learning object classifier to improve e-waste collection planning[J]. Waste Management, 2020, 109: 1-9. doi: 10.1016/j.wasman.2020.04.041
[12] 张睿萍, 宁芊, 雷印杰, 等. 基于改进Mask R-CNN的生活垃圾检测[J]. 计算机工程与科学, 2022, 44(11): 2003-2009.
[13] LU W S, CHEN J J, XUE F. Using computer vision to recognize composition of construction waste mixtures: A semantic segmentation approach[J]. Resources, Conservation and Recycling, 2022, 178: 106022. doi: 10.1016/j.resconrec.2021.106022
[14] 邢洁洁, 谢定进, 杨然兵, 等. 基于YOLOv5s的农田垃圾轻量化检测方法[J]. 农业工程学报, 2022, 38(19): 153-161.
[15] WANG C Y, BOCHKOVSKIY A, LIAO H. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 7464-7475.
[16] 赵元龙, 单玉刚, 袁杰. 改进YOLOv7与DeepSORT的佩戴口罩行人跟踪[J]. 计算机工程与应用, 2023, 59(6): 10.
[17] WANG Y, FU B, Fu L W, et al. In situ sea cucumber detection across multiple underwater scenes based on convolutional neural networks and image enhancements[J]. Sensors, 2023, 23(4): 2037. doi: 10.3390/s23042037
[18] WANG J Q, CHEN K, LIU Z W, et al. CARAFE: content-aware reassembly of features[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019: 3007-3016.
[19] 陈范凯, 李士心. 改进yolov5的无人机目标检测算法[J]. 计算机工程与应用, 2023, 59(18): 218-225.
[20] GENNARI M, FAWCETT R, PRISACARIU V A. DSConv: effificient convolution operator[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 5148-5157.
[21] 王浩, 吕晓琪, 谷宇. 基于语义融合与多尺度注意力的红外行人检测[J/OL]. 激光杂志. http://kns.cnki.net/kcms/detail/50.1085.tn.20230213.1802.006.html.
[22] 肖振久, 林渤翰, 曲海成. 改进YOLOv7的SAR舰船检测算法[J]. 计算机工程与应用, 2023, 59(15): 243-252.
[23] REN S Q, HE K W, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.
[24] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 21-37.
[25] LIU S T, HUANG D, WANG Y H. Receptive field block net for accurate and fast object detection[C]//European Conference on Computer Vision, 2018: 385-400.
[26] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]// Proceedings of the IEEE international conference on computer vision, 2017: 2980-2988.