Spatial-channel transformer network based on mask-RCNN for efficient mushroom instance segmentation

Edible mushrooms are rich in nutrients;however,harvesting mainly relies on manual labor.Coarse localization of each mushroom is necessary to enable a robotic arm to accurately pick edible mushrooms.Previous studies used detection algorithms that did not consider mushroom pixel-level information.When these algorithms are combined with a depth map,the information is lost.Moreover,in instance segmentation algorithms,convolutional neural network(CNN)-based methods are lightweight,and the extracted features are not correlated.To guarantee real-time location detection and improve the accuracy of mushroom segmentation,this study proposed a new spatial-channel transformer network model based on Mask-CNN(SCT-Mask-RCNN).The fusion of Mask-RCNN with the self-attention mechanism extracts the global correlation outcomes of image features from the channel and spatial dimensions.Subsequently,Mask-RCNN was used to maintain a lightweight structure and extract local features using a spatial pooling pyramidal structure to achieve multiscale local feature fusion and improve detection accuracy.The results showed that the SCT-Mask-RCNN method achieved a segmentation accuracy of 0.750 on segm_Precision_mAP and detection accuracy of 0.638 on Bbox_Precision_mAP.Compared to existing methods,the proposed method improved the accuracy of the evaluation metrics Bbox_Precision_mAP and segm_Precision_mAP by over 2%and 5%,respectively.