부산대학교 도서관

Oil is of great significance in the key components like the hydraulic damper of high-speed trains and its leaks suggest that some key components may malfunction, bringing potential danger to the safety of the train. It is indispensable to diagnose oil leakage timely and faults can be discovered by detecting oil stains to avoid possible accidents caused by the breakdown. But it is challenging to discover the oil stains due to their irregularity of shape and diversity of size in complex environments. To deal with these intractable problems, we propose the Multifaceted Feature Aggregation Network (MFANet) which is composed of Multifaceted Refinement Feature (MRF) module, Cross Layer Feature Attention (CLFA) module, and Cross Layer Feature Enhancement (CLFE) module. The MRF solves the limitation of the single convolution method’s inability to catch changeable oil stains better and captures more expressive features. The CLFA and the CLFE obtain richer information and compensate for the shortcomings of insufficient information on single-layer features. Furthermore, we propose a loss function to guide the learning of our network in the case of the unbalanced dataset, which surpasses the useful Binary Cross-entropy loss. We conduct experiments based on four widely used backbone networks and achieve cutting-edge performance with fewer FLOPs and Parameters compared with many advanced methods. In addition, we perform our MFANet on several challenging saliency detection datasets, the rail surface defects detection dataset, and the COCO-Stuff segmentation dataset, which outperforms some progressive approaches and demonstrates powerful generalization capability and adaptation.