With the development of deep learning, object detection algorithms based on horizontal box are widely used in the field of damage identification. However, damages can be in any direction and position, and they are not necessarily horizontal or vertical. This article proposes a bolt damage identification network, namely, orientation-aware center point estimation network, which models a damage as a center point of its rotated bounding box. The proposed orientation-aware center point estimation network uses deep layer aggregation network to search center points and regress to all other damage properties, such as size and angle. A loss function is designed to improve the optimization efficiency of network. Orientation-aware center point estimation network is applied to bolt damage detection, and comparison with the well-known Faster Region-Convolutional Neural Network (a benchmark using horizontal bounding box) demonstrates the accuracy of the proposed method. Finally, videos were utilized to verify the capability of the proposed orientation-aware center point estimation network in real-time detection of bolt damages.

随着深度学习的发展，基于水平盒的目标检测算法被广泛应用于损伤识别领域。但是，损坏可以在任何方向和位置发生，并且不一定是水平或垂直的。本文提出了一种螺栓损伤识别网络，即定向感知中心点估计网络，该模型将损伤建模为其旋转边界框的中心点。拟议的定向感知中心点估计网络使用深层聚合网络搜索中心点，并回归到所有其他损坏属性，例如大小和角度。设计损失函数以提高网络的优化效率。定向感知中心点估计网络应用于螺栓损坏检测，并与著名的Faster Region-卷积神经网络（使用水平边界框作为基准）进行比较，证明了该方法的准确性。最后，视频被用来验证所提出的定向感知中心点估计网络在实时检测螺栓损坏中的能力。