[2] LECUN Y, BENGIO Y. Convolutional networks for images, speech, and time-series[M]. ARBIB M A. The Handbook of Brain Theory and Neural Networks(1995).

[3] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 20, 273-297(1995).

[4] EBRAHIMI M A, KHOSHTAGHAZA M H, MINAEI S et al. Vision-based pest detection based on SVM classification method[J]. Computers and Electronics in Agriculture, 137, 52-58(2017).

[5] SETHY P K, BHOI C, BARPANDA N K et al. Pest detection and recognition in rice crop using SVM in approach of bag-of-words[C](2017).

[6] LIU Z C, ZHANG G H, YANG H Y et al. Application of object detection algorithm in identification of rice weevils and maize weevils[C], 76-80(2018).

[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] LIU J, WANG X W. Tomato diseases and pests detection based on improved Yolov3 convolutional neural network[J]. Frontiers in Plant Science, 11, 898(2020).

[9] REDMON J, FARHADI A. YOLOv3: An incremental improvement[J]. arXiv, 1804. 02767(2018).

[10] YUAN Z M, YUAN H J, YAN Y X et al. Automatic recognition and classification of field insects based on lightweight deep learning model[J]. Journal of Jilin University (Engineering and Technology Edition), 51, 1131-1139(2021).

[11] CHEN J W, LIN W J, CHENG H J et al. A smartphone-based application for scale pest detection using multiple-object detection methods[J]. Electronics, 10, 372(2021).

[12] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection[J]. arXiv, 2004. 10934(2020).

[13] ZHA M F, QIAN W B, YI W L et al. A lightweight YOLOv4-based forestry pest detection method using coordinate attention and feature fusion[J]. Entropy, 23, 1587(2021).

[14] HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).

[15] WANG C Y, LIAO H Y M, WU Y H et al. CSPNet: A new backbone that can enhance learning capability of CNN[C], 1571-1580(2020).

[16] HE K M, ZHANG X Y, REN S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015).

[17] LIU S, QI L, QIN H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[18] LIN T Y, DOLLÁR P, GIRSHICK R et al. Feature pyramid networks for object detection[C], 936-944(2017).

[19] ZHENG Z H, WANG P, LIU W et al. Distance-IoU loss: Faster and better learning for bounding box regression[C], 12993-13000(2020).

[20] LAN X T, GUO Z H, LI C H. Attention and feature fusion for aircraft target detection in optical remote sensing images[J]. Chinese Journal of Liquid Crystals and Displays, 36, 1506-1515(2021).

[21] XIE S H, ZHANG W Z, CHEN P et al. Firesmoke detection model based on YOLOv4 with channel attention[J]. Chinese Journal of Liquid Crystals and Displays, 36, 1445-1453(2021).

[22] WOO S, PARK J, LEE J Y et al. CBAM: Convolutional block attention module[C], 3-19(2018).

[23] WANG Q L, WU B G, ZHU P F et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C](2020).

[24] JIANG R Q, PENG Y P, XIE W X et al. Improved YOLOv4 small target detection algorithm with embedded scSE module[J]. Journal of Graphics, 42, 546-555(2021).

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

[26] ROMDHANE T F, ALHICHRI H, OUNI R et al. Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss[J]. Computers in Biology and Medicine, 123, 103866(2020).

[27] DONG Q Q, LIU J F, HAO L G et al. Student action recognition based on improved SSD algorithm[J]. Computer Engineering and Design, 42, 2924-2930(2021).

[28] ZUO Y Z. Pest recognition system based on deep learning[D](2018).

[29] ZUO T X, HUANG G Y, JIA L P et al. Application of pest detector in forest pests monitoring and control of Hongtashan nature reserve[J]. Forest Inventory and Planning, 43, 121-124(2018).

[30] LIU W, ANGUELOV D, ERHAN D et al. SSD: Single shot multibox detector[C], 21-37(2016).