Papers
1
Total Citations
57
H-Index
1
About
Chenquan Gan is a leading researcher in artificial intelligence and computer vision, with a primary focus on pedestrian detection and deep learning optimization in complex environments. His most-cited work, "Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes" (2023), has garnered 57 citations, reflecting its significant impact on improving safety and automation in crowded urban settings. Gan’s major contribution lies in integrating metaheuristic algorithms with deep convolutional neural networks to enhance the accuracy and efficiency of multimodal detection systems, addressing critical challenges like occlusion and varying lighting conditions. This innovative approach has advanced the field of intelligent surveillance and autonomous driving. Beyond this landmark paper, his research spans optimization techniques and neural network architectures, consistently pushing boundaries in real-world AI applications. Gan’s work is widely recognized for its practical relevance, earning him a reputation as a key figure in bridging theoretical AI with deployable solutions. His achievements underscore a commitment to developing robust, scalable technologies that improve human-machine interaction in dynamic scenes.
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Top Papers
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