This invention relates to the field of image recognition, and more particularly to an image recognition method for the health status of
marine aquaculture fish. First, based on the acquired raw RGB
color image, instance-level segmentation is performed to obtain the category
label,
confidence score, and pixel-level binary
mask for each fish instance. Based on the
confidence score, a
confidence score filtering process is performed. After obtaining the fish instances that pass the confidence
score filtering, image preprocessing is performed to obtain a normalized
color vector and a preprocessed RGB
color image. Then, based on the normalized
color vector and the preprocessed RGB
color image, a posture-sensitive morphology-color joint
health index extraction algorithm is used to obtain a health-sensitive feature index. Finally, based on the health-sensitive feature index, a judgment result is obtained. This method solves the technical problems of morphological indicators becoming ineffective due to fish posture
distortion, the inability to quantify abnormal color features such as red spots and
white spots, and the
impact of
fish species differences on the universality and accuracy of the assessment results.