School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China.
Jiangsu Province Engineering Research Center of High-Level Energy and Power Equipment, Changzhou University, Changzhou 213164, China.
Sensors (Basel). 2022 Nov 14;22(22):8801. doi: 10.3390/s22228801.
Existing detection methods face a huge challenge in identifying insulators with minor defects when targeting transmission line images with complex backgrounds. To ensure the safe operation of transmission lines, an improved YOLOv7 model is proposed to improve detection results. Firstly, the target boxes of the insulator dataset are clustered based on K-means++ to generate more suitable anchor boxes for detecting insulator-defect targets. Secondly, the Coordinate Attention (CoordAtt) module and HorBlock module are added to the network. Then, in the channel and spatial domains, the network can enhance the effective features of the feature-extraction process and weaken the ineffective features. Finally, the SCYLLA-IoU (SIoU) and focal loss functions are used to accelerate the convergence of the model and solve the imbalance of positive and negative samples. Furthermore, to optimize the overall performance of the model, the method of non-maximum suppression (NMS) is improved to reduce accidental deletion and false detection of defect targets. The experimental results show that the mean average precision of our model is 93.8%, higher than the Faster R-CNN model, the YOLOv7 model, and YOLOv5s model by 7.6%, 3.7%, and 4%, respectively. The proposed YOLOv7 model can effectively realize the accurate detection of small objects in complex backgrounds.
现有的检测方法在针对具有复杂背景的输电线路图像进行目标检测时，对于带有轻微缺陷的绝缘子识别存在巨大挑战。为了确保输电线路的安全运行，提出了一种改进的 YOLOv7 模型，以提高检测结果。首先，基于 K-means++对绝缘子数据集的目标框进行聚类，生成更适合检测绝缘子缺陷目标的锚框。其次，在网络中添加坐标注意力（CoordAtt）模块和 HorBlock 模块。然后，在通道和空间域中，网络可以增强特征提取过程中有效特征的强度，削弱无效特征的强度。最后，使用 SCYLLA-IoU（SIoU）和焦点损失函数来加速模型的收敛，并解决正负样本不平衡的问题。此外，为了优化模型的整体性能，改进非极大值抑制（NMS）的方法，以减少缺陷目标的意外删除和误检。实验结果表明，我们的模型的平均精度为 93.8%，分别比 Faster R-CNN 模型、YOLOv7 模型和 YOLOv5s 模型高 7.6%、3.7%和 4%。所提出的 YOLOv7 模型可以有效地实现复杂背景中小目标的准确检测。
