Dynamic Adaptive Enhancement for Power Scene Images DA-IOD Algorithm for Quantitative Target Detection

Aiming to address the problems of insufficient adaptability to new targets and inability to perform incremental detection in power scenarios using traditional target detection technologies, this paper proposes a Dynamic Adaptive Incremental Object Detection algorithm for power scene images (DA-IOD). Firstly, a Task-Aligned Adaptive Feature Decoupling (TAFD) module is adopted to enhance the feature representation capability. Secondly, an Ultra-lightweight and Effective Dynamic Upsampler (DySample) is introduced to reduce computational burden and latency. Finally, a Complete Intersection over Union (CIoU) regression loss function is designed to modify the loss function of the baseline incremental Efficient-IOD algorithm, which further improves the detection accuracy of the proposed algorithm. In the 3+3 single-step incremental scenario of the Guangdong Power Grid Smart On-site Operation Dataset, out proposed method achieves an improvement of 2.4 percentage points in the mean Average Precision (mAP) when compared with the baseline algorithm.