基于改进Faster R-CNN模型的水面漂浮物检测方法

River Drifting Garbage Detection Based on the Improved Faster R-CNN

水面环境治理长期以来都是生态环保的一项重要工作。然而,人工清理水面垃圾的方式难以满足实际工作需求。近年来,深度学习驱动的目标检测算法被成功应用在诸多领域。为解决传统检测方法效率低的问题,本文提出了一种水面漂浮物检测方法。该方法以Faster R-CNN模型为基础,对其主干网络做了改进。在自建的小型水面漂浮物数据集上进行实验,模型识别精度达到77.02%,相较于其它模型有至少2.56%的提升。此外,大量对比实验表明,该模型具有良好的检测性能,基本满足实际需求。

River governance has always been a significant task of ecological environmental protection. However, manual cleaning for river drifting garbage is difficult to meet the actual work needs. In recent years, object detection algorithms have been successfully applied in many fields. To solve the problem of low efficiency of traditional methods, this paper proposes a detection method for river drifting garbage. This method is based on the Faster R-CNN model and improves its backbone network. The experiments are performed on the self-built small drifting garbage data set, and the model recognition accuracy reaches 77.02%, which is 2.56% higher than other classical models. Finally, some comparison experiments show that the proposed model has good detection performance and basically meets the actual needs.