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

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.