基于深浅层特征融合的舰船要害关键点检测算法

导引头对舰船要害部位的精确检测能力是精确制导武器的核心技术之一。针对导引头对舰船要害部位检测精度低、模型参数冗余、相对运动导致舰船图像尺度、角度变化剧烈等问题, 提出了基于深浅层特征融合的舰船要害关键点检测算法。首先，采用多尺度特征融合模块融合不同感受野的有效信息; 其次，利用SoftPool池化改善下采样导致的信息损失, 利于区分相似关键点; 然后，引入深度可分离卷积降低参数冗余, 结合轻量化注意力机制增强有效特征表达; 最后，利用在线难例挖掘改善样本不均衡, 提升收敛效果。改进后的舰船要害关键点检测算法准确率提升4.4%, 算法兼具检测精度与速度优势, 鲁棒性较好。

Key-points detection algorithm based on fusion of deep and shallow features for warship's vital part

The precision detection capability of seeker to target the warship's vital parts is one of the core technologies of precision-guidance weapons. Aiming at the problems of seeker's low detection accuracy, redundant model parameters and drastic changes of images' scale and angle caused by relative motion, the key-points detection algorithm based on fusion of deep and shallow features for warship's vital part is proposed. Firstly, the multi-scale features fusion module is used to fuse the effective information of different receptive fields. Secondy, SoftPool is used to reduce the information loss caused by down sampling, which is conducive to distinguish similar key-points. Then, the depthwise separable convolution is introduced to decrease the parameter redundancy, and the lightweight attention mechanism is combined to enhance the expression of effective features. Finally, the online hard example mining is applied to improve the sample imbalance and accelerate the convergence. The accuracy rate of improved warship's key-points detection algorithm increases by 4.4%. The algorithm not only has the advantages of detection accuracy and detection speed, but also has better robustness.