基于改进YOLOv7的毛虾捕捞渔船作业目标检测与计数方法

Target detection and counting method for Acetes chinensis fishing vessels operation based on improved YOLOv7

渔船捕捞信息量化是开展限额捕捞精细化管理的前提,为解决中国毛虾限额捕捞目标识别和信息统计量化问题,研究了在中国毛虾限额捕捞渔船上安装电子监控(electronic monitoring,EM)设备,并基于YOLOv7提出一种改进的目标检测算法(YOLOv7-MO)和目标计数算法(YOLOv7-MO-SORT)。YOLOv7-MO目标检测算法采用MobileOne作为主干网络,在输出端head部分加入C3模块,并完成剪枝操作;YOLOv7-MO-SORT目标计数算法将SORT(simple online and realtime tracking)算法中的Fast R-CNN替换为YOLOv7-MO,用于检测捕捞作业中抛出的锚和装有毛虾的筐。采用卡尔曼滤波和匈牙利匹配算法对检测到的目标进行跟踪预测,设置碰撞检测线、时间戳、阈值和计数器,实现对捕捞作业过程中渔获毛虾筐数和下网数量计数。结果表明:1)改进后的YOLOv7-MO在测试集上的平均检测精度、召回率、F1得分分别达到了97.3%,96.0%,96.6%,相比YOLOv7模型分别提升了2.0、1.1和1.5个百分点。2)改进后的YOLOv7-MO模型大小、参数量和浮点运算数分别为64.0 MB、32.6 M、39.7 G,相比YOLOv7模型分别缩小了10.2%、10.6%和61.6%。3)以YOLOv7-MO为检测器的SORT算法毛虾捕捞作业计数准确率在统计毛虾筐数和下网数量上分别达到80.0%和95.8%。YOLOv7-MO在提高检测精度的同时减轻了模型量级,提高了检测效率。结果表明,该研究能够为实现渔船捕捞作业信息记录自动化和智能化提供方法,为毛虾限额捕捞管理提供决策参考依据。

Overfishing has been one of the greatest risks to marine biodiversity in the world in recent years.Thus,the production of many catches is in a yearly decline,including Acetes chinensis.At the same time,an Acetes chinensis quota has been introduced to promise the marine conservation of biodiversity in China in 2020.Accurate and rapid quantification of fishing vessel fishing information can be one of the most important prerequisites to implementing the fine management of quota fishing.This study aims to perform the target identification and statistics quantification of Acetes chinensis quota fishing.An electronic monitoring(EM)device was installed on Acetes chinensis quota fishing vessels to monitor the main operation process of fishing vessels.An improved target detection algorithm(YOLOv7-MO)and target counting algorithm(YOLOv7-MO-SORT)using YOLOv7 were proposed to realize the target detection and statistics of Acetes chinensis quota fishing vessels.The YOLOv7-MO target detection algorithm used the MobileOne as the backbone network.The C3 modules were added to the head part of the output during the pruning operation.The YOLOv7-MO-SORT target counting algorithm was selected to replace the Faster R-CNN from the SORT(Simple Online and Realtime Tracking)algorithm replaced by YOLOv7-MO for the detection of anchors thrown during fishing operations and baskets containing Acetes chinensis.Kalman filtering and Hungarian matching algorithms were used to track and predict the detected targets,according to the characteristics of actual production operations.The collision detection lines,timestamps,thresholds,and counters were set to count the number of baskets of Acetes chinensis caught and nets during the fishing operation.The results show:(1)The improved YOLOv7-MO was achieved in average detection accuracy,recall,and F1 score of 97.3%,96.0%,and 96.6%,respectively,on the test set,which were improved by 2.0,1.1 and 1.5 percentage points,compared with the original model.(2)The improved YOLOv7-MO model size,the number of parameters,and the number of floating-point operations were 64.0 MB,32.6 M,and 39.7 G,respectively,which were 10.2%,10.6%,and 61.6%smaller than those of the YOLOv7 model.(3)The accuracy of the SORT algorithm Acetes chinensis fishing operation count with YOLOv7-MO as the detector reached 80.0%and 95.8%in counting the number of Acetes chinensis baskets and the number of nets,respectively.YOLOv7-MO reduced the model magnitude while improving the detection accuracy and efficiency.The SORT algorithm with the YOLOv7-MO as the detection head was also achieved in more accurate statistical quantification of the main operational information of fishing vessels.This function can be expected to facilitate the management and recording of fishing vessel operations,in order to avoid some drawbacks of the traditional manual recording of fishing vessel operations.The gross shrimp basket count statistics can provide better convenience to calculate the fishing parameters,such as the gross shrimp CPUE.The identification can be realized to count the fishing vessel operation of hairy shrimp fishing.The finding can also provide a strong reference to realize the automation and intelligence of recording fishing operations in offshore vessels,particularly for the decision-making on the hairy shrimp quota fishing.