RESEARCH ON DRIED DAYLILY GRADING BASED ON SSD DETAIL DETECTION WITH FEATURE FUSION

Daylily is widely used in medicine and diet therapy. In order to prolong the preservation period of daylily and make better use of its edible value, most of the daylily on the market are dried vegetables. Aiming at the problems of small size of dried daylily, similar color and texture between dried daylily, and difficulty in grading, this study proposes a method for grading dried daylily based on SSD. In the backbone feature extraction stage, the original backbone network VGG16 is replaced with the residual network model ResNet50 to realize the feature extraction of dried daylily. ResNet50 can deepen the network better and is more suitable for dried daylily feature extraction. Secondly, a feature fusion layer is added to improve the problem of insufficient utilization of shallow features in SSD network, which is more suitable for detail detection and improves the accuracy of dried daylily grading. Finally, the input image size is selected [512,512] to increase the image pixels, so that the network can capture more details of the dried daylily to improve the detection accuracy. The results show that the grading precision of the improved SSD algorithm is significantly improved compared with the traditional SSD, and the mean average precision is increased by 4.17%. At the same time, the same data set was used to test on the YOLOv5 model. Compared with YOLOv5s, YOLOv5s-CA and YOLOv5s-CBAM, the mean average precision was increased by 18.32%, 21.82% and 22.02% respectively, which further verified the precision and feasibility of the method and provided effective technical support for the grading of dried daylily.