Search

Abstract Identifying the ash accumulation generated on the water-cooled walls of the waste incinerator is essential for the cleanup by the robotic arm. This paper improves a new algorithm based on YOLOv8n, which can identify the ash accumulation position on the water-cooled wall quickly and accurately. Firstly, the multi-scale fusion image enhancement algorithm is used to improve the sharpness and contrast of the image and enrich the details of the image. Secondly, the backbone feature extraction network of YOLOv8n is replaced by Mobilenetv3 network, which reduces the parameters in the model greatly. Finally, this paper improves a new attention mechanism AWGAM (Add Weight Global Attention Mechanism) based on GAM (Global Attention Mechanism), which can better integrate the feature information between different dimensions and improve the learning ability of the model. AWGAM is added to the backbone of the model. The experimental results show that compared with the original YOLOv8n model, the improved YOLOv8n model has 59.9% fewer parameters, 4.4% higher precision, 8.8% higher recall, 3.2% higher mAP50 (mean Average Precision) and 8.8% higher mAP50-95. This model has made remarkable progress on the basis of the original algorithm, and has strong competitiveness compared with other advanced target detection models. The lightweight and high accuracy of ash accumulation detection offered by the proposed model presents promising applications in ash accumulation detection tasks of water-cooled walls.

Identifying the ash accumulation generated on the water-cooled walls of the waste incinerator is essential for the cleanup by the robotic arm. This paper improves a new algorithm based on YOLOv8n, which can identify the ash accumulation position on the water-cooled wall quickly and accurately. Firstly, the multi-scale fusion image enhancement algorithm is used to improve the sharpness and contrast of the image and enrich the details of the image. Secondly, the backbone feature extraction network of YOLOv8n is replaced by Mobilenetv3 network, which reduces the parameters in the model greatly. Finally, this paper improves a new attention mechanism AWGAM (Add Weight Global Attention Mechanism) based on GAM (Global Attention Mechanism), which can better integrate the feature information between different dimensions and improve the learning ability of the model. AWGAM is added to the backbone of the model. The experimental results show that compared with the original YOLOv8n model, the improved YOLOv8n model has 59.9% fewer parameters, 4.4% higher precision, 8.8% higher recall, 3.2% higher mAP50 (mean Average Precision) and 8.8% higher mAP50-95. This model has made remarkable progress on the basis of the original algorithm, and has strong competitiveness compared with other advanced target detection models. The lightweight and high accuracy of ash accumulation detection offered by the proposed model presents promising applications in ash accumulation detection tasks of water-cooled walls. [ABSTRACT FROM AUTHOR]