SR-Det:Towards Robust Detection of Slender and Rotated Objects in Industrial Scene

Though object detection has been widely used in the industrial scene, it still faces the detection problems of crack defects with slender and rotated characteristics. On the one hand, traditional horizontal anchor methods are usually hard to precisely locate the object. On the other hand, CNNs (Convolutional Neural Networks) perform poorly in terms of feature extraction from rotated objects. In addition, normal loss functions are insensitive to slender objects. To address these, this paper proposes a Slender and Rotated Detector (SR-Det) for robust slender and rotated object detection. Specifically, the Rotated Region Calibration (RRC) is designed, which takes horizontal proposals with different scales and aspect ratios as inputs and outputs the corresponding rotation proposals. Then, the Rotated Angle Proposal Align (RAP-Align) is presented to guarantee the quality of extracted feature information. Finally, the Rotated intersection over union(R-IoU) based on Intersection Over Union (IoU) strategy is proposed for guiding the model to maximize the area between predicted box and Ground Truth box. The experiments on metal cans and curtain walls datasets have shown that the method proposed achieves state-of-the-art performance, demonstrating the effectiveness of the proposed algorithm.