水下鱼病分割管理数据

使用水下摄像头和计算机视觉技术，对鱼群进行实时监控，自动检测异常行为或表征，识别并分割出病变区域，如溃疡、出血或其他异常情况，为进一步的诊断提供依据。水下鱼病分割管理数据可以帮助水产养殖相关企业向更智能、更高效、更可持续的方向发展，同时为整个产业链创造新的价值和机遇。1. 数据来源：通过水下摄像头捕捉鱼群图像。2. 数据预处理：对捕获的图像进行去噪处理；增强图像对比度；调整亮度和对比度以提高图像质量；3. 数据处理：提取鱼病特征，如颜色、纹理、形状等；使用YOLO算法进行图像分割，确识别和定位病变区域。病变区域的轮廓信息被存储在“位置信息”字段中，字段包含一个二维坐标数组，其中每个元素是一个 [x, y] 坐标对，表示多边形轮廓的一个顶点。这些坐标点按特定顺序排列，当依次连接时，形成一个闭合的多边形，精确地描绘出病变区域的边界。同时，基于提取的特征，系统为每个病变区域分配相应的标签，如"ulcer"表示溃疡。4.数据应用：通过水下鱼病分割管理数据，可以更好地分析鱼群健康状况，做出有效的养殖管理决策，提高养殖效率和鱼群健康水平。

Leveraging underwater cameras and computer vision techniques, this underwater fish disease segmentation and management dataset enables real-time monitoring of fish schools, automatically detects abnormal behaviors or manifestations, identifies and segments lesion regions including ulcers, hemorrhages, and other anomalies, providing a basis for further diagnosis.

The dataset can help aquaculture-related enterprises move towards a smarter, more efficient, and more sustainable development direction, while creating new value and opportunities for the entire aquaculture industrial chain.

1. Data Source: Fish school images are captured via underwater cameras.
2. Data Preprocessing: Denoising processing is conducted on the captured images; image contrast is enhanced, and brightness adjustments are made to improve overall image quality.
3. Data Processing: Features related to fish diseases such as color, texture, shape, etc., are extracted. The YOLO algorithm is used for image segmentation to accurately identify and locate lesion regions. The contour information of each lesion region is stored in the "location information" field, which contains a 2D coordinate array. Each element in the array is an [x, y] coordinate pair representing a vertex of the polygonal contour. These coordinate points are arranged in a specific order, and when connected sequentially, they form a closed polygon that precisely delineates the boundary of the lesion region. Meanwhile, based on the extracted features, the system assigns corresponding labels to each lesion region, such as "ulcer" for ulcerous lesions.
4. Data Application: By utilizing this dataset, the health status of fish schools can be better analyzed, effective aquaculture management decisions can be made, and both aquaculture efficiency and the health level of fish schools can be improved.

AI搜集汇总

数据集介绍

特点

该数据集是一个用于水产养殖中鱼病检测和管理的数据集，包含520条数据，每条数据包括图片名称、尺寸、标签名称和病变区域的位置信息。数据集通过计算机视觉技术对鱼群进行实时监控，自动检测和分割病变区域，如溃疡、出血等，为水产养殖提供智能化的管理支持。