Dengyong Zhang1,2, Huaijian Pu1,2, Feng Li1,2,*, Xiangling Ding3, Victor S. Sheng4
CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3439-3454, 2023, DOI:10.32604/cmc.2023.027267
Abstract Now object detection based on deep learning tries different strategies. It uses fewer data training networks to achieve the effect of large dataset training. However, the existing methods usually do not achieve the balance between network parameters and training data. It makes the information provided by a small amount of picture data insufficient to optimize model parameters, resulting in unsatisfactory detection results. To improve the accuracy of few shot object detection, this paper proposes a network based on the transformer and high-resolution feature extraction (THR). High-resolution feature extraction maintains the resolution representation of the image. Channels and spatial attention are… More >
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