曾涛, 宗钊辉, 陈桢禄, 等.烟草害虫绿色防控技术研究进展[J].安徽农业科学, 2022, 50(1):15-17
, 36.
张善文, 许新华, 齐国红, 等.基于可形变VGG-16模型的田间作物害虫检测方法[J].农业工程学报, 2021, 37(18):188-194.
徐聪, 王旭启, 刘裕.一种改进可形变FCN的农作物害虫检测方法[J].江苏农业科学, 2022, 50(9):211-219.
刘凯旋, 黄操军, 李亚鹏, 等.一种基于级联R-CNN的水稻害虫检测算法[J].黑龙江八一农垦大学学报, 2021, 33(5):106-111
, 134.
肖德琴, 黄一桂, 张远琴, 等.基于改进Faster R-CNN的田间黄板害虫检测算法[J].农业机械学报, 2021, 52(6):242-251.
REN S Q, HE K M, GIRSHICK R, et al.Faster r-cnn:Towards real-time object detection with region proposal networks[J].Advances in Neural Information Processing Systems, 2015, 39(6):1137-1149.
陈向东, 邓江洪.基于显著性检测的害虫图像自动分割算法研究[J].中国粮油学报, 2021, 36(1):181-186.
苗海委, 周慧玲.基于深度学习的粘虫板储粮害虫图像检测算法的研究[J].中国粮油学报, 2019, 34(12):93-99.
林俊宇.基于机器视觉的烟虫检测方法研究[D].武汉:华中科技大学, 2020.
洪金华, 忻惠琴, 陆海华, 等.基于YOLOv3模型的卷烟厂烟虫识别方法[J].烟草科技, 2020, 53(9):77-84.
REDMON J, FARHADI A.YOLOv3:An incremental improvement[J/OL].arXiv:1804.02767, 2018[2023-03-10].https://arxiv.org/abs/1804.02767.
何雨, 田军委, 张震, 等.YOLOv5目标检测的轻量化研究[J].计算机工程与应用, 2023, 59(1):92-99.
杨锦辉, 李鸿, 杜芸彦, 等.基于改进YOLOv5s的轻量化目标检测算法[J].电光与控制, 2023, 30(2):24-30.
YANG Q S, LI W K, YANG X F, et al.Improved YOLOv5 method for detecting growth status of apple flowers[J].ComputEngAppl, 2022, 58(4):237-246.
DEVRIES T, TAYLOR G W.Improved regularization of convolutional neural networks with cutout[J/OL].arXiv:1708.04552, 2017[2023-03-10].https://arxiv.org/abs/1708.04552.
XU R J, LIN H F, LU K J, et al.A forest fire detection system based on ensemble learning[J].Forests, 2021, 12(2):217.
MEHTA S, RASTEGARI M, SHAPIRO L, et al.Espnetv2:A light-weight, power efficient, and general purpose convolutional neural network[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.[S.l.:s.n.], 2019:9190-9200.
HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.[S.l.:s.n.], 2016:770-778.
IOFFE S, SZEGEDY C.Batch normalization:Accelerating deep network training by reducing internal covariate shift[C]//International conference on machine learning.PMLR.[S.l.:s.n.], 2015:448-456.
MASTROMICHALAKIS S.ALReLU:A different approach on Leaky ReLU activation function to improve neural networks performance[J/OL].arXiv:2012.07564, 2020[2023-03-10].https://arxiv.org/abs/2012.07564.
FU J, LIU J, TIAN H J, et al.Dual attention network for scene segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.[S.l.:s.n.], 2019:3146-3154.
DAI Y, LIU W M, WANG H, et al.YOLO-former:Marrying YOLO and transformer for foreign object detection[J].IEEE Transactions on Instrumentation and Measurement, 2022, 71:1-14.
GE Z, LIU S T, WANG F, et al.Yolox:Exceeding yolo series in 2021[J/OL].arXiv:2107.08430, 2021[2023-03-10].https://arxiv.org/abs/2107.08430.
YU J M, ZHANG W.Face mask wearing detection algorithm based on improved YOLOv4[J].Sensors, 2021, 21(9):3263.
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[J/OL].arXiv:2207.02696, 2022[2023-03-10].https://openaccess.thecvf.com/content/CVPR2023/html/Wang_YOLOv7_Trainable_Bag-of-Freebies_Sets_New_State-of-the-Art_for_Real-Time_Object_Detectors_CVPR_2023_paper.html.