轻量级网络算法对输电线路上异物目标的识别

针对输电线路上多种异物所引起的电力巡检问题，可以采用深度学习图像识别方法进行检测。文中提出了一种改进型轻量级网络检测算法模型，通过将YOLOv4的主干特征提取网络替换为轻量级神经网络GhostNet,减少图片输入时计算所产生的特征图冗余；对YOLOv4的PANet模块进行修改，采用深度可分离卷积模块替换其中的普通卷积模块，可减轻参数计算量。结果表明，相比于原YOLOv4检测算法，该改进型算法在IOU阈值取0.5时，平均精准度下降2.1%,但检测速度达到了原算法的2.21倍，参数计算量仅为原算法的17.84%。与其他几种算法的对比表明新算法的参数指标表现满足需求。在维持较高精确度的情况下，文中所提算法的检测速度得到提升，计算量减少，证明了其在目标检测时的有效性与可行性。

Identification of Foreign Objects on Transmission Lines Using Lightweight Network Algorithm

In view of the power inspection problem caused by various foreign objects on the transmission line, the deep learning image recognition method can be used for detection. This study proposes an improved lightweight network detection algorithm model. By replacing the backbone feature extraction network of YOLOv4 with lightweight neural network GhostNet, the redundancy of feature map generated by image input calculation is reduced. The PANet module of YOLOv4 is modified, and the depth separable convolution module is used to replace the common convolution module, which can reduce the amount of parameter calculation. The results show that, compared with the original YOLOv4 detection algorithm, when the IOU threshold is 0.5, the average accuracy of the improved algorithm decreases by 2.1%, but the detection speed is 2.21 times that of the original algorithm, and the parameter calculation amount is only 17.84% of the original algorithm. The comparison with other algorithms shows that the parameter performance of the proposed algorithm meets the demand. Under the condition of maintaining high accuracy, the detection speed of the proposed algorithm is improved and the computation amount is reduced, which proves the effectiveness and feasibility of the proposed algorithm in target detection.