潼南区委宣传部. 潼南: 小柠檬"走出去" 开拓国内国际大市场[J]. 重庆与世界, 2023(12): 56-59.
代小红, 钟光跃, 曹树梅, 等. 安岳柠檬品牌培育现状及建议[J]. 四川农业科技, 2023(1): 119-121.
周冰. 农业机械化助力乡村振兴中的影响与作用[J]. 当代农机, 2024(2): 39-40, 42.
郑太雄, 江明哲, 冯明驰. 基于视觉的采摘机器人目标识别与定位方法研究综述[J]. 仪器仪表学报, 2021, 42(9): 28-51.
LECUN Y, BENGIO Y, HINTON G. Deep Learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
ZHANG X H, WANG H P, XU C A, et al. A Lightweight Feature Optimizing Network for Ship Detection in SAR Image[J]. IEEE Access, 2019, 7: 141662-141678.
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C] //2014 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2014: 580-587.
GIRSHICK R. Fast R-CNN[C] //2015 IEEE International Conference on Computer Vision (ICCV). New York: IEEE, 2015: 1440-1448.
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, 2017, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031
LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single Shot MultiBox Detector[C] // Computer Vision - ECCV 2016. Cham: Springer International Publishing, 2016: 21-37.
REDMON J, FARHADI A. YOLO9000: Better, Faster, Stronger[C] //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2017: 6517-6525.
REDMON J, FARHADI A. YOLOv3: An Incremental Improvement[EB/OL]. (2018-04-08)[2024-03-14]. https://arxiv.org/abs/1804.02767.
BOCHKOVSKIY A, WANG C Y, LIAO H M. YOLOv4: Optimal Speed and Accuracy of Object Detection[EB/OL]. (2020-04-23)[2024-03-14]. https://arxiv.org/abs/2004.10934v1.
REDMON J, DIVVALA S, GIRSHICK R, et al. You Only Look Once: Unified, Real-Time Object Detection[C] //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2016: 779-788.
LI G J, HUANG X J, AI J Y, et al. Lemon-YOLO: An Efficient Object Detection Method for Lemons in the Natural Environment[J]. IET Image Processing, 2021, 15(9): 1998-2009.
LAWAL M O. Tomato Detection Based on Modified YOLOv3 Framework[J]. Scientific Reports, 2021, 11(1): 1447.
GAI R L, CHEN N, YUAN H. A Detection Algorithm for Cherry Fruits Based on the Improved YOLO-V4 Model[J]. Neural Computing and Applications, 2023, 35(19): 13895-13906.
张成尧, 张艳诚, 张宇乾, 等. 基于YOLOv5的咖啡瑕疵豆检测方法[J]. 食品与机械, 2023, 39(2): 50-56, 175.
ZHONG Z Y, YUN L J, CHENG F Y, et al. Light-YOLO: A Lightweight and Efficient YOLO-Based Deep Learning Model for Mango Detection[J]. Agriculture, 2024, 14(1): 140.
RUIZ-PONCE P, ORTIZ-PEREZ D, GARCIA-RODRIGUEZ J, et al. POSEIDON: a Data Augmentation Tool for Small Object Detection Datasets in Maritime Environments[J]. Sensors, 2023, 23(7): 3691.
LI P, ZHENG J S, LI P Y, et al. Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8[J]. Sensors, 2023, 23(15): 6701.
李茂, 肖洋轶, 宗望远, 等. 基于改进YOLOv8模型的轻量化板栗果实识别方法[J]. 农业工程学报, 2024, 40(2): 1-9.
LUO Q, WU C B, WU G J, et al. A Small Target Strawberry Recognition Method Based on Improved YOLOv8n Model[J]. IEEE Access, 2024, 12: 14987-14995.
SUNKARA R, LUO T. No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects[C] // Machine Learning and Knowledge Discovery in Databases. Cham: Springer Nature Switzerland, 2023: 443-459.
OUYANG D L, HE S, ZHANG G Z, et al. Efficient Multi-Scale Attention Module with Cross-Spatial Learning[C] //ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New York: IEEE, 2023: 1-5.
TONG Z, CHEN Y, XU Z, et al. Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism[EB/OL]. (2020-01-24)[2024-03-19]. https://arxiv.org/abs/2301.10051.
赵德安, 吴任迪, 刘晓洋, 等. 基于YOLO深度卷积神经网络的复杂背景下机器人采摘苹果定位[J]. 农业工程学报, 2019, 35(3): 164-173.
BRAUWERS G, FRASINCAR F. A General Survey on Attention Mechanisms in Deep Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(4): 3279-3298.
GUO M H, XU T X, LIU J J, et al. Attention Mechanisms in Computer Vision: A Survey[J]. Computational Visual Media, 2022, 8(3): 331-368.
NIU Z Y, ZHONG G Q, YU H. A Review on the Attention Mechanism of Deep Learning[J]. Neurocomputing, 2021, 452: 48-62.
LAI Q X, KHAN S, NIE Y W, et al. Understanding More about Human and Machine Attention in Deep Neural Networks[J]. IEEE Transactions on Multimedia, 2020, 23: 2086-2099.
WAN D H, LU R S, SHEN S Y, et al. Mixed Local Channel Attention for Object Detection[J]. Engineering Applications of Artificial Intelligence, 2023, 123: 106442.
YANG H, YUAN C F, ZHANG L, et al. STA-CNN: Convolutional Spatial-Temporal Attention Learning for Action Recognition[J]. IEEE Transactions on Image Processing, 2020, 29: 5783-5793.
JIN X, XIE Y P, WEI X S, et al. Delving Deep into Spatial Pooling for Squeeze-and-Excitation Networks[J]. Pattern Recognition, 2022, 121: 108159.
赵茂程, 邹涛, 齐亮, 等. 基于MobileViT-CBAM的枇杷表面缺陷检测方法[J]. 农业机械学报, 2024, 7(2): 1-10.
LI X, HU X L, YANG J. Spatial Group-Wise Enhance: Improving Semantic Feature Learning in Convolutional Networks[EB/OL]. (2020-04-23)[2024-06-14]. https://arxiv.org/abs/1905.09646v2.