TCBGY-Net : Ferrography wear particle detection network based on self-attention mechanism and multi-scale feature fusion

Abstract The intelligent identification of wear particles in ferrography is a critical bottleneck that hampers the development and widespread adoption of ferrography technology. To address challenges such as false detection, missed detection of small wear particles, difficulty in distinguishing overlapping and similar abrasions, and handling complex image backgrounds, this paper proposes an algorithm called TCBGY-Net for detecting wear particles in ferrography images. Firstly, we integrate a Transformer module based on self-attention mechanism with the C3 module at the end of the backbone network to form a C3TR module. This integration enhances the global feature extraction capability of the backbone network and improves its ability to detect small target wear particles. Secondly, we introduce CBAM attention mechanism into the neck network to enhance salience for detecting wear particles while suppressing irrelevant information interference. Furthermore, multi-scale feature maps extracted by the backbone network are fed into BiFPN feature fusion network to enhance the model's ability to detect wear particle feature maps at different scales. Lastly, Ghost modules are introduced into both the backbone network and the neck network to reduce their complexity and improve detection speed. Experimental results demonstrate that TCBGY-Net achieves outstanding accuracy in detecting wear particles against complex backgrounds with an [email protected] value of 98.3%, which is 10.2% higher than YOLOv5s; moreover it also outperforms most current mainstream algorithms in terms of detection speed with up to 89.2FPS capability; thus providing conditions for subsequent real-time online monitoring of changes in wear particles and fault diagnosis in ship power systems.