本文系统探讨了利用深度学习技术（YOLOv11）自动识别河湖生态环境危害的创新方法，构建了包含12类危害目标的专用数据集WATER-DET，通过迁移学习实现污染类别、排污口及岸线侵占等多目标检测，模型F1分数达0.72，为水环境智能监测提供了高效技术方案。

In tomato cultivation, various diseases significantly impact tomato quality and yield. The substantial scale differences among diseased leaf targets pose precise detection and identification ...

Detect vehicle license plates in videos and images using the tensorflow/object_detection API. Train object detection models for license plate detection using TFOD API, with either a single detection ...

Data labeling is essential to the development process of machine learning models. It ensures data accuracy by providing properly annotated datasets. The rapid advancement in AI technology has made it ...

In order to more accurately detect the accuracy of word-wheel water meter digits, 2000 water meter pictures were produced, and an improved Faster-RCNN algorithm for detecting water meter digits was ...

# wget -O weights/darknet53.conv.74.weights https://pjreddie.com/media/files/darknet53.conv.74 # wget -O weights/darknet19_448.conv.23.weights https://pjreddie.com ...

Weed suppression is one of the greatest factors affecting crop production. The weeds could compete with crops for water, light, fertilizer, growth space, other nutrients, etc., resulting in reduction ...