LAUSR

Foreign object intrusion detection in overhead power systems (OPSs) is critical during preventive maintenance in catenary inspection. However, the OPS images characteristics, including appearance degradation and noisy representations, increase the difficulty of foreign object detection. In this article, a sparse cross attention (SCA) based transformer detector (SCATD) is developed for detecting foreign objects in OPSs. Specifically, a two-stage refinement architecture is proposed for extracting the features of foreign objects in OPS images. In the first stage, a spatiotemporal enhanced (STE)-convolutional neural network (CNN) is proposed to leverage feature-level spatiotemporal coherence across frames to emphasize the spatial responses of foreign objects. Then, a spatial memory (SM) based feature aggregation (SMFA) module is constructed to iteratively update the feature affinities during training. Moreover, the SCA network sparsely builds different weak transformer detectors to produce weak predictive results. All the predictive results are fused to achieve the better predictive performance via voting schemes in an adaptive way. Finally, our SCATD is compared with state-of-the-art deep learning-based object detection algorithms. Experiments on real OPS dataset demonstrate the high effectiveness of the proposed SCATD scheme in detecting foreign instances in OPSs.