文献类型：期刊文献

英文题名：SkeletonDETR: A novel multimodal fusion based object detection framework for chemical safety applications☆

作者：Tang, Yudi[1];Wang, Bing[1];He, Wangli[1];Qian, Feng[1];Liu, Zhen[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2025

卷号：160

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20253218934526);WOS:【SCI-EXPANDED(收录号:WOS:001545635100001)】；

基金：star This work is supported by National Key Research and Development Program of China under Grant 2018AAA0101602, National Natural Science Foundation of China (61922030) .

语种：英文

外文关键词：Object detection; Pose estimation; Small target; Local feature

摘要：Regarding the object detection algorithm in field operations in chemical plants, how to accurately detect objects carried and used by construction workers has become a crucial challenge in safety monitoring. Current object detection algorithms usually perform well for large objects but can easily ignore small objects, particularly under partial occlusion. Moreover, existing methods fail to recognize the significance of workers' pose information during the construction process, which provides significant benefits for the detection task, especially when the targets for detection are closely associated with the construction workers. To solve this problem, we proposed a novel multimodal fusion based object detection framework, which can effectively use human pose information to improve the detection effect of small targets and occlusions. Furthermore, we propose a multimodal sampling module to fully utilize the features of different modalities to enhance the encoder's ability to aggregate features. Compared with the baseline model, our proposed method achieves an 8.3% improvement in small object detection performance. Comprehensive experiments demonstrate that our proposed method outperforms existing efficient models, especially in field operation in chemical plants.