The invention discloses a multi-target fish identification method and
system based on
deep learning. The method comprises the following steps: firstly, detecting fish targets in a video frame through a YOLO network and constructing a candidate set; then extracting bounding boxes, color features and area information of each target, and constructing a fusion
feature vector; furthermore, in the inter-frame matching stage, dynamic weighted association is carried out by combining the position distance and the color similarity, and robust identity matching and ID management are realized; meanwhile, calculating and
smoothing a motion track based on a continuous frame center
point sequence, and optimizing speed and
direction angle data; and finally, outputting a
visualization result with the target identifier and the motion trail, and generating global and individual statistical reports. By fusing color features, position information and
kinematics parameters, high-precision detection, stable tracking and multi-dimensional behavior statistics of ornamental fishes such as
fancy carp in a complex
underwater scene are realized, and the problems of frequent ID switching, high missing detection and
false alarm rate, poor track quality and the like in the prior art are solved.