多尺度特征融合的Anchor-Free轻量化舰船要害部位检测算法

反舰导弹对舰船要害部位的精确打击能力是精确制导武器的关键技术之一。针对反舰导弹导引头对舰船要害部位检测精度低、特征提取能力不足，预测框的处理降低检测速度等问题，提出了一种多尺度特征融合的Anchor-Free轻量化舰船要害部位检测算法。由于舰船要害部位检测数据具有多尺度、多角度特性，引入多尺度特征融合模块，综合利用不同感受野的检测信息，优化特征提取；利用高效轻量化注意力机制改进Hourglass结构中的跨层连接，提升检测精度，降低算法总参数量；使用迁移学习有效提升算法收敛效果。在建立的舰船要害部位检测数据集和公开的PASCAL VOC数据集进行实验，检测准确率分别提升了4.41%和5.57%，分析算法参数与运算量，设计了模块消融实验，论证了所提算法的有效性。 

Warship's vital parts detection algorithm based on lightweight Anchor-Free network with multi-scale feature fusion

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.