关键词: Retina-Net, 图像处理, 卷积神经网络, 目标检测, 感受野, 软池化

Abstract: In urban planning and garden landscape, the artificial lawn plays a role in beautifying the environment, but the breeding of all kinds of lawn weeds seriously damages the ornamental ability of the landscape lawn. The artificial identification of weeds is timeconsuming and laborious, which affects the subsequent weeding efficiency. Therefore, based on the research results of deep learning, this study improves the existing Retina-Net target detection model by extracting the target image feature information of the training set, adding multiscale receptive field, improving the soft pool layer and other methods to improve the ability of weed detection and species discrimination of the model, which is conducive to the efficient development of subsequent weeding work. The recognition rates of six kinds of weeds in the final experiment were 85.3%, 84%, 89.6%, 86.7%, 95.1% and 91.5% respectively. Compared with other mainstream target detection algorithms, the recognition rate is improved by 2.2% to 9.3% respectively.

Key words: Retina-Net, image processing, CNN, object detection, receptive field, soft pooling