基于YOLOv7的垃圾焚烧分类检测研究
Research on Classification and Detection of Waste Incineration Based on YOLOv7
DOI: 10.12677/CSA.2023.132027, PDF,   
作者: 谢能勇：安徽理工大学经济与管理学院，安徽 淮南
关键词: 目标检测；YOLOv7；实时监测；Target Detection； YOLOv7； Real-Time Monitoring
摘要: 为实现自动垃圾检测和抓取工作，解决垃圾站操作工人的不准确性和劳动力，本文使用基于YOLOv7算法的实时垃圾检测算法，可以通过垃圾池区域中的摄像头对各分区的垃圾进行实时拍照检测，通过吊车夹爪对待抓取的垃圾进行实时抓取进入待焚烧区域，有效提高生产效率。实验结果表明，本文算法在训练100轮次的mAP值为87.7%，可较好地满足实际垃圾实时工业处理的需求。
Abstract: In order to realize automatic garbage detection and grasping work and solve the inaccuracy and labor of the workers operating the refuse collection point, this paper uses a real-time garbage detection algorithm based on the YOLOv7 algorithm, which can detect the garbage in each partition by taking real-time photos through the camera in the garbage pool area and grasp the garbage to be grasped into the area to be incinerated in real-time through the crane jaws, effectively improving the production efficiency. The experimental results show that the algorithm of this paper has a mAP value of 87.7% in 100 rounds of training, which can better meet the needs of real garbage in real-time industrial processing.
文章引用：谢能勇. 基于YOLOv7的垃圾焚烧分类检测研究[J]. 计算机科学与应用, 2023, 13(2): 270-280. https://doi.org/10.12677/CSA.2023.132027

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