论文标题
计算机视觉系统以计算甲壳类幼虫
Computer vision system to count crustacean larvae
论文作者
论文摘要
截至2017年，鱼类产品占全球人类饮食的16％。计数作用是生产和生产这些产品的重要组成部分。种植者必须准确计算鱼类，以便这样做技术解决方案。开发了两个计算机视觉系统，以自动计算在工业池塘中生长的甲壳类幼虫。第一个系统包括带有3024x4032分辨率的iPhone 11相机，该摄像头在室内条件下从工业池塘中获取了图像。该系统进行了两个实验，其中第一张包括在一天的9,10的一天内使用iPhone 11相机在特定照明条件下获得的200张图像。在第二个实验中，用两个设备iPhone 11和索尼DSCHX90V摄像机拍摄了一个幼虫工业池。使用第一个设备（iPhone 11）测试了两个照明条件。在每种情况下，都获得了110张图像。该系统的精度为88.4％的图像检测。第二个系统包括DSLR Nikon D510摄像头，具有2000x2000分辨率，在工业池塘外进行了七次实验。在幼虫生长阶段的第1天获取图像，从而获得了总共700张图像。该系统的密度为50的精度为86％。一种基于Yolov5 CNN模型开发的算法，该算法自动计算两种情况的幼虫数量。此外，在这项研究中，开发了幼虫生长功能。每天，几个幼虫是从工业池塘手动取的，并在显微镜下进行了分析。一旦确定生长阶段，就获得了幼虫的图像。每个幼虫的长度都是通过图像手动测量的。最合适的模型是Gompertz模型，其拟合指数的优点为r平方为0.983。
Fish products account for about 16 percent of the human diet worldwide, as of 2017. The counting action is a significant component in growing and producing these products. Growers must count the fish accurately, to do so technological solutions are needed. Two computer vision systems to automatically count crustacean larvae grown in industrial ponds were developed. The first system included an iPhone 11 camera with 3024X4032 resolution which acquired images from an industrial pond in indoor conditions. Two experiments were performed with this system, the first one included 200 images acquired in one day on growth stages 9,10 with an iPhone 11 camera on specific illumination condition. In the second experiment, a larvae industrial pond was photographed for 11 days with two devices an iPhone 11 and a SONY DSCHX90V cameras. With the first device (iPhone 11) two illumination conditions were tested. In each condition, 110 images were acquired. That system resulted in an accuracy of 88.4 percent image detection. The second system included a DSLR Nikon D510 camera with a 2000X2000 resolution with which seven experiments were performed outside the industrial pond. Images were acquired on day 1 of larvae growing stage resulting in the acquisition of a total of 700 images. That system resulted in an accuracy of 86 percent for a density of 50. An algorithm that automatically counts the number of larvae was developed for both cases based on the YOLOv5 CNN model. In addition, in this study, a larvae growth function was developed. Daily, several larvae were taken manually from the industrial pond and analyzed under a microscope. Once the growth stage was determined, images of the larva were acquired. Each larva's length was measured manually from the images. The most suitable model was the Gompertz model with a goodness of fit index of R squared of 0.983.
