Improved YOLOv5s-based detection method for termitomyces albuminosus

Termitomyces albuminosus is one species of agaric fungus in the family Agaricaceae in food production. However, it is a high demand to increase the current model detection accuracy, particularly under the complex planting environment, such as the variety of light and dark, the low recognition of soil and termitomyces albuminosus, the dense growth distribution and the serious shelter. In this study, target detection was proposed using an improved YOLOv5s. Firstly, RFBSE (Receptive Field Block Squeeze and Excitation) module was integrated into the backbone network. The human sensory field was then simulated to enhance the consistent contribution of different pixels to neural nodes in the process of network feature extraction. The edge features were highlighted to focus on the key areas. The irrelevant disturbances were suppressed using the module, such as the background. The attention mechanism of the channel was applied to gain the weight of different channels for the adaptive calibration of channel characteristic response. The channel was also enhanced to contain the important characteristic information of termitomyces albuminosus. As such, the high characteristic expression was achieved in the termitomyces albuminosus. Secondly, a multi-branch sampling DCSPP (Double Conv Spatial Pyramid Pooling) Pooling module was designed to perform the multiple sampling, in order to fuse the multiple receptive fields and then strengthen the relation between local and global information. The expression ability of the feature layer was enriched to improve the detection accuracy. Thirdly, the RFP (Recursive Feature Pyramid) structure was adopted in the neck network. The number of samples cannot increase, due to the usual interclass occlusion between termitomyces albuminosus. Previously, the network paid attention to the same image twice, because the context information around the occlusion samples was very important, and the feedback feature layer generated in the FPN structure was re-fed back to the backbone network for the Recursive computation. The neuronal activation was able to learn the correspondence and selectively inhibit, in order to improve the detection ability of the dense occluded sample of termitomyces albuminosus. The semantic information transmission was also promoted to enhance the context information near the occluded sample. At the same time, the cascaded RFP structure and the network fusion structure were lightened to reduce the calculation of parameters and memory usage. The ablation results showed that the RFBSE module, multi-branch pool module, and recursive pyramid structure shared different effects on the model. Specifically, the average precision mAP, precision, and recall rate of the final model reached 90.8%, 86.5%, and 84.8%, respectively. Each index of the improved model was improved, compared with the original one. The higher quality of the bounding box was achieved to detect the occluded target, where the mAP, accuracy, and recall were improved by 2.7, 3.8, and 3.9 percentage points, respectively. At last, the detection test of termitomyces albuminosus was carried out in different environments and shelter conditions by hardware platform model deployment. The visualized results showed that the detection rate of the model was more than 90%, which verified the validity of the model. The experimental results show that the improved model can be expected to accurately and rapidly identify the termitomyces albuminosus in a complex environment. The finding can provide technical support for the development of the termitomyces albuminosus harvesting robots.