Abstract
Object detection is one of the research hotspots in computer vision. However, most existing object detectors struggle with the identification of small targets. Therefore, the paper proposes two modules: the MDFFAM (Multi-Directional Feature Fusion Attention Mechanism) and the LKSPP (Large Kernel Spatial Pyramid Pooling), to enhance the detector's effectiveness in identifying subtle faults on the surface of mechanical equipment. LKSPP aims to expand the receptive field to capture high-level semantic features through large kernels. Meanwhile, the MDFFAM allows the network to efficiently utilize spatial location information and adaptively recognize detection priorities. In the detection task, MDFFAM effectively captures feature information in three spatial directions: width, height, and channel, with the location information fully utilized to establish stable long-range dependencies. Moreover, LKSPP boasts a larger receptive field and imposes less computational burden compared to the SPPCSPC by YOLOv7. Finally, experiments demonstrate that the proposed module effectively improves the detection accuracy for small targets, surpassing the state-of-the-art object detector, YOLOv7. Remarkably, MDFFAM incurs almost negligible computational overhead.