Lujuan Deng, Ruochong Fu*, Zuhe Li, Boyi Liu, Mengze Xue, Yuhao Cui
CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4071-4089, 2024, DOI:10.32604/cmc.2024.048200
Abstract Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the… More >
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