Fish are indicative species with a relatively balanced ecosystem. Underwater target fish detection is of great significance to fishery resource investigations. Traditional investigation methods cannot meet the increasing requirements of environmental protection and investigation, and the existing target detection technology has few studies on the dynamic identification of underwater fish and small targets. To reduce environmental disturbances and solve the problems of many fish, dense, mutual occlusion and difficult detection of small targets, an improved CME-YOLOv5 network is proposed to detect fish in dense groups and small targets. First, the coordinate attention (CA) mechanism and cross-stage partial networks with 3 convolutions (C3) structure are fused into the C3CA module to replace the C3 module of the backbone in you only look once (YOLOv5) to improve the extraction of target feature information and detection accuracy. Second, the three detection layers are expanded to four, which enhances the model’s ability to capture information in different dimensions and improves detection performance. Finally, the efficient intersection over union (EIOU) loss function is used instead of the generalized intersection over union (GIOU) loss function to optimize the convergence rate and location accuracy. Based on the actual image data and a small number of datasets obtained online, the experimental results showed that the mean average precision (mAP@0.50) of the proposed algorithm reached 94.9%, which is 4.4 percentage points higher than that of the YOLOv5 algorithm, and the number of fish and small target detection performances was 24.6% higher. The results show that our proposed algorithm exhibits good detection performance when applied to densely spaced fish and small targets and can be used as an alternative or supplemental method for fishery resource investigation.
In this paper, based on the improved DeepSORT algorithm, four target species of passing fish (Schizothorax o’connori, Schizothorax waltoni, Oxygymnocypris stewartii and Schizopygopsis younghusbandi) from a fishway project in the middle reaches of the Y River were used to achieve dynamic identification and automatic counting of passing fish species using fishways monitoring video. This method used the YOLOv5 model as the target detection model. In view of the large deformation by fish body twisting, the network structure of the re-identification (ReID) model was deepened to strengthen the feature extraction ability of the model. It was proposed to identify and track fish that cross the line by setting a virtual baseline to achieve the dynamic identification of fish species passing and the automatic counting of upward and downward quantities. The results showed that 1) among the five models, YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, the highest value of mean average precision (mAP) was 92.8% achieved by the YOLOv5x model. Specifically, recognition accuracies of 96.95%, 94.95%, 88.79%, and 91.93% were recorded for Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii, respectively. 2) The error rate of the improved ReID model was 20.3%, which was 20% lower than that before the improvement, making it easier for the model to obtain target features. 3) The average accuracy of the improved DeepSORT algorithm for counting four target fishes was 75.5%, among which the accuracy of Schizothorax o’connori, S. waltoni, S. younghusbandi and O. stewartii were 83.6%, 71.1%, 68.1%, and 79.3%, respectively. Meanwhile, the running speed was 44.6 fps, which met the real-time monitoring. This method is the first to implement intelligent identification of the target passing fish in fishways projects, which can accumulate long series monitoring data for fishways operation and management and provide a technical solution and reference for the work related to the realization of intelligent and informative passing fish monitoring.
The phenomenon origin, process and resolution of the psychological contradiction of the character Joe in the Soul by Pixar was analyzed by Lacanian mirror theory in the study, to express the psychological contradiction of the group of black people who depend on a specific social class. (1) Based on Lacanian theory of the real world (Real), the primitive desires origin of the character Joe was analyzed.(2) Psychological contradictions of the group represented by Joe was studied in accordance with imaginary, which reflects the social malpractice. (3)Result of resolving the psychological contradiction of Joe in Symbolic was studied; Joe became an ideal subject in the 'Ideal American dream' as expressed by Pixar. Therefore, the value and significance of Soul in the portrayal character's psychological field obtained can regurgitation-feed the social reality issues.
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