Agricultural Machinery Engineering Research and Design Institute, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel). 2023 Jul 12;23(14):6325. doi: 10.3390/s23146325.
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.
鱼体长度是水产养殖工程中的一个重要监测参数。然而，传统的手动测量方法被发现效率低下，并且对鱼类有害。为了克服这些缺点，本文提出了一种非接触测量方法，利用双目立体视觉准确测量水下鱼类的体长。双目相机捕获 RGB 和深度图像，以获取鱼的 RGB-D 数据，然后使用对比度自适应Grab Cut 算法选择性地分割 RGB 图像。为了确定鱼的状态，采用骨骼提取算法来处理弯曲身体的鱼。然后分析并校正水的折射引起的误差。最后，从 RGB 图像中提取最佳测量点，并将其转换为 3D 空间坐标，以计算鱼的长度，并为此开发了测量软件。实验结果表明，鱼体长测量的平均相对百分比误差为 0.9%。本文提出的方法满足水产养殖测量的精度要求，同时方便实施和应用。
