<p>High-voltage overhead transmission line insulator strings are persistently exposed to harsh environments such as heavy rain, lightning, strong ultraviolet radiation, and significant temperature variations, which can readily lead to the deterioration of their electrical and mechanical performance. This degradation may result in defects like flashover and physical damage, posing a severe threat to grid security. To achieve efficient and accurate identification of defects in insulator strings, this paper proposes a detection method that integrates RGB color space analysis with multi-scale feature compensation. Firstly, preliminary segmentation of the insulator region is performed based on RGB component thresholds, and coarse localization of the insulator string is accomplished by combining morphological operations with non-overlapping window texture analysis. Subsequently, the minimum enclosing horizontal rectangle of the target is generated, enabling fine localization of the insulator string and segmentation of the horizontal rectangular region based on the texture features within the bounding box. Finally, a multi-scale compensation detection network is constructed, which employs multi-level detection heads to differentially compensate for missing high- and low-frequency information, thereby enhancing defect recognition accuracy. Experiments demonstrate that the proposed method significantly outperforms eight mainstream object detection models in terms of mean Average Precision (mAP). Through rigorous experimental validation, at an Intersection over Union (IoU) threshold of 0.5, the mAP of our method shows an improvement of approximately 4.1% points compared to the best-performing baseline method.</p>