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