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The fish target detection algorithm lacks a good quality data set, and thealgorithm achieves real-time detection with lower power consumption on embeddeddevices, and it is difficult to balance the calculation speed andidentification ability. To this end, this paper collected and annotated a dataset named "Aquarium Fish" of 84 fishes containing 10042 images, and based onthis data set, proposed a multi-scale input fast fish target detection network(BTP-yoloV3) and its optimization method. The experiment uses Depthwiseconvolution to redesign the backbone of the yoloV4 network, which reduces theamount of calculation by 94.1%, and the test accuracy is 92.34%. Then, thetraining model is enhanced with MixUp, CutMix, and mosaic to increase the testaccuracy by 1.27%; Finally, use the mish, swish, and ELU activation functionsto increase the test accuracy by 0.76%. As a result, the accuracy of testingthe network with 2000 fish images reached 94.37%, and the computationalcomplexity of the network BFLOPS was only 5.47. Comparing the YoloV3~4,MobileNetV2-yoloV3, and YoloV3-tiny networks of migration learning on this dataset. The results show that BTP-Yolov3 has smaller model parameters, fastercalculation speed, and lower energy consumption during operation while ensuringthe calculation accuracy. It provides a certain reference value for thepractical application of neural network.

Research on Optimization Method of Multi-scale Fish Target Fast Detection Network