| Citation: | LI Chenxuan, GU Jiaojiao, WANG Lei, et al. Warship's vital parts detection algorithm based on lightweight Anchor-Free network with multi-scale feature fusion[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(10): 2006-2019. doi: 10.13700/j.bh.1001-5965.2021.0050(in Chinese)
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One of the key technologies of precision-guidance weapons is the anti-ship missile's ability to strike vital parts of a warship with pinpoint accuracy. Aiming at the problems of low detection accuracy, insu-fficient ability in feature extraction and the processing of the generated-anchors reduces the detection speed in anti-ship missile seekers, a warship's vital parts detection algorithm based on a lightweight Anchor-Free network with multi-scale feature fusion is proposed. Due to the multi-scale and multi-angle characteristics of the vital parts detection data, the multi-scale feature fusion module is introduced to optimize the feature extraction by comprehensively using the detection information of different receptive fields. To boost the detection accuracy and reduce the total parameters of the algorithm, the skip connections in Hourglass are enhanced by using the efficient and lightweight attention mechanism. The transfer-learning is used to improve the convergence of this algorithm effectively. Experiments were carried out on the dataset of the warship's vital parts and the PASCAL VOC. Experimental results show the mAP is increased by 4.41% and 5.57% respectively. The algorithm's parameters and the computation are analyzed. The module ablation experiments are designed to demonstrate the effectiveness of the algorithm.
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