Remote intelligent identification of suspended foreign objects in substation inspection images based on edge optimization faster R-CNN algorithm

To avoid the interference of blur and noise in substation inspection images, which can impact the recognition of foreign objects hanging within these images, and to swiftly generate precise candidate areas for the accurate identification and localization of such foreign objects, a substation inspection method based on the edge-optimized Faster R-CNN algorithm is proposed. This method represents a remote, intelligent identification approach for foreign objects suspended in inspection images. Using drones equipped with cameras, the method performs substation inspection tasks. After collecting substation inspection images, bilateral filters and adaptive edge compensation techniques are applied to enhance the images, thereby removing interference and noise. This enhancement improves the image contrast, and the refined substation inspection image is then fed into the Faster R-CNN algorithm. The model performs convolution, feature extraction, classification, and other relevant operations on the image, ultimately outputting the substation inspection image along with remote intelligent identification of the suspended foreign object. Consequently, to enhance the accuracy of candidate areas during the remote intelligent identification of foreign objects hanging in substation inspection images, the Faster R-CNN algorithm is optimized using an edge algorithm. The experimental results demonstrate that this method possesses a robust capability to enhance substation inspection images, can effectively remotely identify various types of foreign objects hanging in these images, provides valuable data on foreign object hangings for substation operations and arcs, and exhibits strong applicability.