Crab Flavored Mushroom Detection Method Based on Improved YOLOv8 Convolutional Neural Network

A crab flavored mushroom recognition and detection method based on improved YOLOv8 convolutional neural network was proposed to address issue of better yield estimation and real-time detection of growth status in production process of crab flavored mushrooms．This method refered to PASCAL VOC dataset format and constructed a crab flavored mushroom target detection dataset．Original algorithm was improved by adding CBAM attention mechanism, and model performance was compared with Faster R-CNN, SSD（single shot multibox detector）, original YOLOv8 and other algorithms for experimental testing．Experimental results showed that improved algorithm was significantly superior to other algorithms, with an mean average precision（mAP）and detection speed of 95% and 91 frames/s on the test set, respectively．This detection accuracy and detection time met real-time recognition and detection task of crab flavored mushrooms, providing theoretical and technical support for estimating yield of crab flavored mushrooms and improving production management level．