关键词: 残差网络, 注意力机制, 损失函数, 目标检测, 草莓图像识别

Abstract: In view of the current situation that it is difficult to recognize ripe strawberry fruit due to the factors such as natural light illumination, branch and leaf shading, and interfruit shading, this paper proposes a ripe strawberry target detection algorithm that combines deep residual network and attention mechanism. In this paper, the deep residual network Resnet50, which had stronger information expression capability, was invoked to replace the backbone network underlying the SSD target detection algorithm model, and the attention mechanism method of channel and spatial direction was processed to obtain the information feature extraction map after the residual network structure and the new convolutional feature extraction layer, and the RC-SSD target detection model that could accurately implement the mature strawberry target detection was established. The experimental results showed that the RC-SSD algorithm model in this paper had less number of parameters than the models Faster R-CNN, YOLOv3 and SSD-VGG models, and the average accuracy mean mAP was improved by 46.05%, 10.16% and 5.77%, respectively, in which the recognition accuracy of mature strawberry reached 99.04%, and compared with the lightweight network structure model SSD-Mobilenetv2, the RC-SSD algorithm model improved the accuracy by 20.20% with a 25 fps reduction in FPS relative to the lightweight network model, and the FPS reached 86 fps on the GPU running device.

Key words: residual network, attention mechanism, loss function, object detection, strawberry image recognition