Transformation‐Equivariant Network Fused With Multi‐Stage Cascade Attention for Point Cloud Object Detection

ABSTRACT Object detection of unmanned firefighting vehicles faces challenges such as strong electromagnetic interference, drastic lighting changes and dynamic object variations. To address these issues, we propose a two‐stage 3D point cloud object detection algorithm called TED‐CasA‐Fusion. The first stage uses the transformation‐equivariant detector backbone that explicitly models rotation/reflection equivariance via weight‐sharing sparse convolutions, which improves detection robustness to dynamically transformed objects. The second stage introduces a cascade attention‐based multistage refinement network that aggregates cross‐stage object features through cascade attention modules, which effectively enhances feature representation for multiscale objects. Furthermore, the second stage integrates weighted bounding box voting to address training imbalance due to dense nearby and sparse distant point distributions, thereby improving detection accuracy for distant and sparse targets. Comparative experiments were conducted on the KITTI dataset and a self‐collected firefighting dataset between the proposed algorithm and some state‐of‐the‐art algorithms. Results show that the proposed algorithm achieves the best 3D detection accuracy for hard‐category objects on the KITTI dataset and also outperforms other detection approaches on the firefighting dataset. This work offers an efficient and reliable solution to environmental perception of unmanned firefighting vehicles.