广东工业大学学报2024，Vol.41Issue(2)：93-100,8.DOI:10.12052/gdutxb.230027
SR-Det:面向工业场景下细长和旋转目标的鲁棒检测
SR-Det:Towards Robust Detection of Slender and Rotated Objects in Industrial Scene
摘要
Abstract
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.关键词
目标检测/损失函数/旋转不变性Key words
object detection/loss function/rotation invariance分类
信息技术与安全科学引用本文复制引用
何森柏,程良伦,黄国恒,伍志超,叶颂航..SR-Det:面向工业场景下细长和旋转目标的鲁棒检测[J].广东工业大学学报,2024,41(2):93-100,8.基金项目
佛山市重点领域科技攻关资助项目(2020001006832) （2020001006832）
