Abstract
［Objective ］ In order to improve the recognition accuracy of different kinds of marine fish,an improved YOLOv 5s marine fish species recognition method was proposed..［Methods ］ K-means++algorithm was used to cluster the real frames of marine fish,and more matching anchor frames were obtained with the self built data set.CIoU Loss function was replaced by SIoU Loss function as the boundary box regression algorithm to improve the accuracy and rate of convergence of the boundary box regression.Improved some C 3 modules of the backbone network,and integrated CA coordination attention mechanism into the C 3 module,which improved the recognition accuracy and detection speed of the model while reducing the number of model parameters.Finally,optimized the path aggregation network of the model to enhance the feature fusion ability of the network.［Results］ The experimental results showed that the improved Our-YOLOv 5s model had a mAP of 98.4% and a detection speed of 64 s-1 in the dataset,which was 2.4% and 6 s-1 higher than the original model,respectively.［Conclusion ］ The model can meet the real-time detection requirements of marine fish.
Publication Date
2-18-2025
First Page
84
Last Page
92
DOI
10.13652/j.spjx.1003.5788.2023.81080
Recommended Citation
Haifeng, ZHANG; Xinchun, LU; Bo, FENG; and Jin, YANG
(2025)
"Marine fish species recognition based on improved YOLOv 5s,"
Food and Machinery: Vol. 40:
Iss.
8, Article 12.
DOI: 10.13652/j.spjx.1003.5788.2023.81080
Available at:
https://www.ifoodmm.cn/journal/vol40/iss8/12
References
[1] 谢晶,谭明堂,杨大章,等.我国渔业仓储保鲜和冷链物流发展现状 [J].包装工程,2021,42(11):1-10.XIE J,TAN M T,YANG D Z,et al.Development status of fisheries storage-preservation and cold chain logistics in China[J].Packaging Engineering,2021,42(11):1-10.
[2] 梁钊董,熊兴国,徐东坡,等.基于形状及纹理特征的淡水鱼种 类 自 动 识 别 方 法 [J].南 京 农 业 大 学 学 报,2021,44(3):576-585.LIANG Z D,XIONG X G,XU D P,et al.An utomatic method for freshwater fish species classification using shape and texture features [J].Journal of Nanjing Agricultural University,2021,44(3):576-585.
[3] 石慧.基于机器视觉的鱼类识别算法研究 [D].徐州:中国矿业大学,2021:13-33.SHI H.Research on fish recognition algorithm based on machine vision [D].Xuzhou:China University of Mining and Technology,2021:13-33.
[4] 戈明辉, 张俊, 陆慧娟. 基于机器视觉的食品外包装缺陷检测算法研究进展 [J].食品与机械,2023,39(9):95-102,116.GE M H,ZHANG J,LU H J.Research progress of food packaging defect detection based on machine vision [J].Food & Machinery,2023,39(9):95-102,116.
[5] LI S S,LI C,YANG Y,et al.Underwater scallop recognition algorithm using improved YOLOv 5[J]. Aquacultural Engineering,2022,98:102273.
[6] RENS 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):106680.
[7] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector [C]// European Conference on Computer Vision.Amsterdam:Springer,2016:21-37.
[8] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:779-788.
[9] 谭鹤群,李玉祥,朱明,等.通过图像增强与改进 Faster-RCNN网络的重叠鱼群尾数检测 [J].农业工程学报,2022,38(13):167-176.TAN H Q,LI Y X,ZHU M,et al.Detecting overlapping fish population using image enhancement and improved Faster-RCNN networks [J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE ),2022,38(13):167-176.
[10] 张岚,邢博闻,李彩,等.采用改进 SSD网络的海参目标检测算法 [J].农业工程学报,2022,38(8):297-303.ZHANG L,XING B W,LI C,et al.Algorithm for detecting sea cucumbers based on improved SSD [J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE ),2022,38(8):297-303.
[11] 张志凯,韩红章,赵雪芊,等.基于改进 YOLOv 3模型的软包装食品自动识别方法 [J].食品与机械,2023,39(5):95-100.ZHANG Z K,HAN H Z,ZHAO X Q,et al.Automatic recognition method for soft packaged food based on improved YOLOv 3 model [J].Food & Machinery,2023,39(5):95-100.
[12] 鄢紫,陈良艳,刘卫华,等.基于 YOLO-FFD 的水果品种和新鲜度识别方法 [J].食品与机械,2024,40(1):115-121.YAN Z,CHEN L Y,LIU W H,et al.Fruit variety and freshness recognition method based on YOLO-FFD [J].Food & Machinery,2024,40(1):115-121.
[13] WANG K,CHEN K,DU H,et al.New image dataset and new negative sample judgment method for crop pest recognition based on deep learning models [J].Ecological Informatics,2022,69:101620.
[14] LI S S,LI Y J,LI Y,et al.YOLO-FIRI:improved YOLOv 5 for infrared image object detection [J].IEEE Access,2021,9:141861.
[15] 武历展,王夏黎,张倩,等.基于优化 YOLOv 5s的跌倒人物目标检测方法 [J].图学学报,2022,43(5):791-802.WU L Z,WANG X L,ZHANG Q,et al.An object detection method of falling person based on optimized YOLOv 5s[J].Journal of Graphics,2022,43(5):791-802.
[16] LIU Y,HE G,WANG Z,et al.NRT-YOLO:improved YOLOv 5 based on nested residual transformer for tiny remote sensing object detection [J].Sensors,2022,22(13):4 953.
[17] YE C L,WANG Y F,TIE M,et al.Steering angle prediction YOLOv 5-based end-to-end adaptive neural network control for autonomous vehicles [J].Proceedings of the Institution of Mechanical Engineers,Part D:Journal of Automobile Engineering,2021,236(9):1053677.
[18] 侯维岩,靳东安,王高杰,等.基于嵌入式系统的智能售货柜目 标 检 测 算 法 [J].电 子 测 量 与 仪 器 学 报,2021,35(10):217-224.HOU W Y,JIN D A,WANG G J,et al.Object detection algorithm of intelligent vending cabinet via embedded system[J].Journal of Electronic Measurement and Instrumentation,2021,35(10):217-224.
[19] 张 德 春,李 海 涛,李 勋,等.基 于 CBAM和BiFPN改 进YoloV 5的渔船目标检测 [J].渔业现代化,2022,49(3):71-80.ZHANG D C,LI H T,LI X,et al.Optimization of YOLOv 5s fish vessel target detection based on CBAM and BiFPN [J].Fishery Modernization,2022,49(3):71-80.
[20] ZHENG Z,WANG P,REN D,et al.Enhancing geometric factors in model learning and inference for object detection and instance segmentation [J].IEEE Transactions on Cybernetics,2021,52(8):8 574-8 586.
[21] DU S,ZHANG B,ZHANG P.Scale-sensitive IOU loss:an improved regression loss function in remote sensing object detection [J].IEEE Access,2021,9:141258.
[22] GAO J B,LIANG J R,LI J L,et al.White-light endoscopic colorectal lesion detection based on improved YOLOv 7[J].Biomedical Signal Processing and Control,2024,90:105897.
[23] 闫钧华,张琨,施天俊,等.融合多层级特征的遥感图像地面弱小目标检测 [J].仪器仪表学报,2022,43(3):221-229.YAN J H,ZHANG K,SHI T J,et al.Multi-level feature fusion based dim small ground target detection in remote sensing images [J].Chinese Journal of Scientific Instrument,2022,43(3):221-229.
[24] HOU Q B,ZHOU D Q,FENG J S.Coordinate attention for efficient mobile network design [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Nashille:CVPR,2021:13 713-13 722.
[25] 钱坤,李晨瑄,陈美杉,等.基于 YOLOv 5的舰船目标及关键部位检测算法 [J].系统工程与电子技术,2022,44(6):1 823-1 832.QIAN K,LI C X,CHEN M S,et al.Ship target and key parts detection algorithm based on YOLOv 5[J].Systems Engineering And Electronics,2022,44(6):1 823-1 832.
[26] GUO S,LI L,GUO T,et al.Research on mask-wearing detection algorithm based on improved YOLOv 5[J].Sensors,2022,22(13):4 933.
[27] TAN M,PANG R,LE Q V.Efficientdet:scalable and efficient object detection [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Seattle:CVPR,2020:10 781-10 790.
[28] YIN X,WU D,SHANG Y,et al.Using an efficient Net-LSTM for the recognition of single cow ’s motion behaviours in a complicated environment [J].Computers and Electronics in Agriculture,2020,177:105707.
[29] 洪洋.森林野火预警的小目标检测算法研究 [D].成都:电子科技大学,2022:23-36.HONG Y.Research on small target detection algorithm for forest wildfire early warning [D].Chengdu:University of Electronic Science and Technology of China,2022:23-36.
[30] 代牮,赵旭,李连鹏,等.基于改进 YOLOv 5的复杂背景红外弱小目标检测算法 [J].红外技术,2022,44(5):504-512.DAI J,ZHAO X,LI L P,et al.Improved YOLOv 5-based Infrared dim-small target detection under complex background[J].Infrared Technology,2022,44(5):504-512.
