CAST-YOLO: An Improved YOLO Based on a Cross-Attention Strategy Transformer for Foggy Weather Adaptive Detection

Both transformer and one-stage detectors have shown promising object detection results and have attracted increasing attention. However, the developments in effective domain adaptive techniques in transformer and one-stage detectors still have not been widely used. In this paper, we investigate this issue and propose a novel improved You Only Look Once (YOLO) model based on a cross-attention strategy transformer, called CAST-YOLO. This detector is a Teacher-Student knowledge transfer-based detector. We design a transformer encoder layer (TE-Layer) and a convolutional block attention module (CBAM) to capture global and rich contextual information. Then, the detector implements cross-domain object detection through the knowledge distillation method. Specifically, we propose a cross-attention strategy transformer to align domain-invariant features between the source and target domains. This strategy consists of three transformers with shared weights, identified as the source branch, target branch, and cross branch. The feature alignment uses knowledge distillation, to address better knowledge transfer from the source domain to the target domain. The above strategy provides better robustness for a model with noisy input. Extensive experiments show that our method outperforms the existing methods in foggy weather adaptive detection, significantly improving the detection results.

Automated summary

Both transformer and one-stage detectors have not widely used effective domain adaptive techniques. In this paper, we propose a novel improved YOLO model called CAST-YOLO, which implements cross-domain object detection through knowledge distillation. Extensive experiments show that our method outperforms existing methods in foggy weather adaptive detection, significantly improving the detection results.