| Citation: | ZHANG Y Z，LI W B，ZHENG T T. Inverted residual target detection algorithm based on LGC[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（6）：1287-1293 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0452
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Target detection based on deep learning is a research hotspot in computer vision. Although existing mainstream detection models usually increase the depth and width of the network to acquire better detection results, it is unamiable to suffer from parameters increasing and detection rate decreasing. To address this problem, an efficient lightweight Ghost convolution (LGC) model, which aims to balance the detection accuracy and speed, and obtain more feature maps with fewer parameters, was proposed by referring to the lightweight idea of Ghost convolution and group convolution. CSPDarkNet53 that was redesigned with the above convolution and an inverted residual structure was introduced to generate an inverted residual feature extraction network to improve the global feature information extraction capability of the model. On this basis, the inverted residual feature extraction network was used as the backbone network of YOLOv4, and depthwise separable convolution was used to reduce the parameters. To improve the overall performance of the algorithm, an inverted residual target detection algorithm was proposed. Experimental results show that compared with the current mainstream target detection algorithm, the proposed algorithm has prominent advantages in the number of model parameters and detection speed under the premise of similar detection accuracy.
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