Fish stock detection algorithm based on zebrafish image features

The computer vision methods are increasingly applied to the study of zebrafish stock behavior. However, due to the large form changes and many occlusions in the swimming process of zebrafish, it is still a very challenging problem to accurately and robustly detect the zebrafish. To solve this problem, a fish stock detection algorithm based on zebrafish image features was proposed. Firstly, by analyzing the target characteristics, a detection method using fish head and fish tail instead of the whole fish was proposed, which solved the problem that the traditional whole fish detection failed in the case of fish stock cross occlusion. Then, the training set was automatically constructed based on zebrafish image features, which avoided the time-consuming and laborious problem of manual annotation in deep learning. Through the processing and verification of the actual zebrafish videos, and compared with the existing algorithms, the proposed method has better experimental effects in the performance indexes such as annotation rate, recall rate and occlusion detection rate (ODR). In terms of annotation performance, the total annotation rate of the proposed automatic annotation method is 87.40%. In terms of the effect of training set, the automatic annotation algorithm combined with manual correction reduces the annotation time by 93.11% compared with the manual annotation method, and the mean average precision (mAP) is 79.80%. In terms of target detection, when the target occlusion rate is 42.72%, the proposed detection algorithm can obtain the recall rate of 82.0% and the occlusion detection rate of 58.02%.