A Defective Bolt Detection Model With Attention-Based RoI Fusion and Cascaded Classification Network

Abstract

In the Unmanned Aerial Vehicle (UAV) transmission line inspection images, the detection of defective small-size object such as bolts on towers is important and challenging. Although using multi-scale features of deep neural network has improved the performance, it is still inadequate in mining fine-grained associations between multi-scale features and dealing with the high similarity between normal and defective bolts. Therefore, this paper proposes an improved defective bolt detection model MARF-CCN, based on Region of Interest (RoI) feature fusion and Cascaded Classification Network (CCN). First, a mixed attention RoI fusion network is built to adaptively compute fine-grained weights for features at different scales of feature pyramid network and enhances the difference between foreground and background. Second, cascaded classification network is designed to divide the original classification results into more easily identifiable categories based on morphological features, which are rectified via a secondary classifier to reduce false detection. Third, this paper defines atypical defects based on occurrence frequency and utilizes Focal Loss to address the resulting imbalanced classification loss. Experiments show that MARF-CCN improves the Average Precision (AP) of defective bolts by 14.33% to 84.40% compared with the commonly used models.