The Surface Crack Extraction Method Based on Machine Learning of Image and Quantitative Feature Information Acquisition Method

In-Water Fish Body-Length Measurement System Based on Stereo Vision

Fish body length is an essential monitoring parameter in aquaculture engineering. However, traditional manual measurement methods have been found to be inefficient and harmful to fish. To overcome these shortcomings, this paper proposes a non-contact measurement method that utilizes binocular stereo vision to accurately measure the body length of fish underwater. Binocular cameras capture RGB and depth images to acquire the RGB-D data of the fish, and then the RGB images are selectively segmented using the contrast-adaptive Grab Cut algorithm. To determine the state of the fish, a skeleton extraction algorithm is employed to handle fish with curved bodies. The errors caused by the refraction of water are then analyzed and corrected. Finally, the best measurement points from the RGB image are extracted and converted into 3D spatial coordinates to calculate the length of the fish, for which measurement software was developed. The experimental results indicate that the mean relative percentage error for fish-length measurement is 0.9%. This paper presents a method that meets the accuracy requirements for measurement in aquaculture while also being convenient for implementation and application.