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To address these issues, we propose a two\u2010stage 3D point cloud object detection algorithm called TED\u2010CasA\u2010Fusion. The first stage uses the transformation\u2010equivariant detector backbone that explicitly models rotation\/reflection equivariance via weight\u2010sharing sparse convolutions, which improves detection robustness to dynamically transformed objects. The second stage introduces a cascade attention\u2010based multistage refinement network that aggregates cross\u2010stage 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\u2010collected firefighting dataset between the proposed algorithm and some state\u2010of\u2010the\u2010art algorithms. Results show that the proposed algorithm achieves the best 3D detection accuracy for hard\u2010category 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.<\/jats:p>","DOI":"10.1049\/cit2.70118","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:18:53Z","timestamp":1773029933000},"page":"564-577","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transformation\u2010Equivariant Network Fused With Multi\u2010Stage Cascade Attention for Point Cloud Object Detection"],"prefix":"10.1049","volume":"11","author":[{"given":"Jiangdong","family":"Wu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering Shanghai Jiao Tong University  Shanghai China"},{"name":"State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University  Shanghai 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