Aiming at the challenges of high efficiency and accuracy of insulator defect detection for embedded devices in resource-constrained and foggy complex environments, this paper proposes a new lightweight detection model, RNSC-YOLOv7-tiny, and achieves important innovative results and practical application value. Firstly, the RepNCSPELAN module is designed by lightweighting the ELAN module in the backbone network, which effectively reduces the number of parameters and computational complexity of the model, while maintaining a significant improvement in detection accuracy. Secondly, the incorporation of the Spatial Group Enhancement module enables the model to focus on the target region overlapping with the background, thereby significantly suppressing the interference of irrelevant information and improving the accuracy of insulator defect localisation and identification. Furthermore, the incorporation of the NWD loss function addresses the issue of gradient vanishing due to deviation points in the detection process, thereby enhancing the overall detection accuracy. Furthermore, the incorporation of the CARAFE upsampling operator enables the model to achieve accurate detection and localisation in low-resolution images and complex foggy environments. The experimental results demonstrate that the RNSC-YOLOv7-tiny model exhibits rapid and highly accurate performance in insulator defect detection, with a detection accuracy of 94.8%. The model comprises 4298150 parameters and 10.5 floating-point operations, yet occupies only 8.69 MB of memory. In comparison to the original YOLOv7-tiny model, the newly proposed model exhibits notable enhancements in several pivotal metrics. The accuracy has been augmented by 3.4%, the number of parameters has been diminished by 28.5%, the number of floating-point operations has been reduced by 19.2%, and the model size has been reduced by 3.01 MB. These outcomes substantiate the algorithm′s high applicability in embedded device environments and its efficacy in practical applications.