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Abstract：
Currently, the intelligent defect detection of massive grid transmission line inspection pictures using AI image recognition technology is an efficient and popular method. Usually, there are two technical routes for the construction of defect detection algorithm models: one is to use a lightweight network, which improves the efficiency, but it can generally only target a few types of defects and may reduce the detection accuracy; the other is to use a complex network model, which improves the accuracy, and can identify multiple types of defects at the same time, but it has a large computational volume and low efficiency. To maintain the model\'s high detection accuracy as well as its lightweight structure, this paper proposes a lightweight and efficient multi type defect detection method for transmission lines based on DCP-YOLOv8. The method employs deformable convolution (C2f_DCNv3) to enhance the defect feature extraction capability, and designs a re-parameterized cross phase feature fusion structure (RCSP) to optimize and fuse high-level semantic features with low level spatial features, thus improving the capability of the model to recognize defects at different scales while significantly reducing the model parameters; additionally, it combines the dynamic detection head and deformable convolutional v3\'s detection head (DCNv3-Dyhead) to enhance the feature expression capability and the utilization of contextual information to further improve the detection accuracy. Experimental results show that on a dataset containing 20 real transmission line defects, the method increases the average accuracy (mAP@0.5) to 72.2%, an increase of 4.3%, compared with the lightest baseline YOLOv8n model; the number of model parameters is only 2.8 M, a reduction of 9.15%, and the number of processed frames per second (FPS) reaches 103, which meets the real time detection demand. In the scenario of multi type defect detection, it effectively balances detection accuracy and performance with quantitative generalizability.
摘要：
目前,利用AI图像识别技术对海量电网输电线路巡检图片进行智能缺陷检测是一种高效、流行的方法。通常,缺陷检测算法模型的构建有两条技术路线:一是使用轻量级网络,这提高了效率，但它通常只能针对少数类型的缺陷，并可能降低检测精度；另一种是使用复杂的网络模型，这提高了准确性，并且可以同时识别多种类型的缺陷，但是它的计算量大，效率低。为了保持模型的高检测精度以及其轻量化结构,提出了一种轻量高效的基于DCP-YOLOv8的输电线路多类型缺陷检测方法。该方法采用可变形卷积(C2f_DCNv3)来增强缺陷特征提取能力,并设计了一种重新参数化的交叉相位特征融合结构(RCSP),将高层语义特征与低层空间特征进行优化和融合,从而提高模型识别不同尺度缺陷的能力，同时显著降低模型参数；它结合了动态检测头和可变形卷积v3的检测头（DCNv3-Dyhead），以增强特征表达能力和上下文信息的利用率，进一步提高检测精度。实验结果表明,在一个包含20条真实输电线路缺陷的数据集上,该方法将平均准确度(mAP@0.5)提高到72.2%，增长4.3%,与最轻的基线YOLOv8n模型相比；模型参数数量仅为2.8M，减少9.15%,每秒处理帧数(FPS)达到103,满足实时检测需求。在多类型缺陷检测的场景中，它有效地平衡了检测精度和性能与定量泛化。
