Fine-grained object detection method using attention mechanism and its application in coal–gangue detection

Abstract

Although the performance of object detectors is constantly improving based on benchmark assessments, how to carry out their industrial deployment remains a challenging task. Considering that fine-grained characteristic is widely existed in industrial object images, we proposed a single-shot fine-grained object detector, and applied it to coal–gangue images in coal preparation plant. Firstly, we employed grouped convolution to build a new convolutional neural network as the backbone of the detector. Secondly, a feature fusion module, which was designed based on spatial attention, was employed to optimize the features extracted by a feature pyramid network. Finally, a feature separation module, which was designed based on channel attention, was employed to alleviate the conflict in detection between the classification task and the localization task. Moreover, the effect of each component was fully demonstrated by ablation study, visualization analysis and hypothesis testing. Compared with other classical detectors, the proposed detector’s APiou=0.5 has increased by an average of approximately 10% in coal–gangue dataset.