Abstract:

As the core of computer vision, target detection is widely used in these aspects, such as face recognition, face tracking, and large-scale scene recognition. In the field of one-stage, the detection speed and detection performance of SSD algorithm are relatively prominent but there are still false detection and missed detection against the complicated background of multi-target detection. Regarding this problem, it proposes multi-target detection method based on improved SSD algorithm to improve testing performance through optimizing network in internal SSD and increasing the applicability of training samples. It adopts modifying network output and optimizing P-NMS algorithm in internal SSD to unify network structure and reduce false detection. It adds ARConv and limiting function to optimize training samples and decrease training time of model. During the period of testing, it proposes single picture batch testing method to effectively increase model recall rate.The experimental results show that improved algorithm has stronger robustness and it can effectively decrease false detection rate to promote network performance.

Key words: multi-target detection, optimization of Single Shot MultiBox Detector（SSD） algorithm, Anti Rotation Convolution（ARConv）, Probability-Non Maximum Suppression（P-NMS） algorithm, picture batch testing

摘要：

目标检测作为计算机视觉的核心，在人脸识别、人脸跟踪、大规模场景识别等方面具有广泛应用，其中One-stage领域的SSD算法检测速度和检测性能较为突出，但在环境较为复杂的多目标检测情况下仍会出现误检和漏检。针对这一问题，提出一种改进SSD算法的多目标检测方法，通过优化SSD内部网络和提高样本适用性的方式改善检测性能；其中，采用修改网络输出和添加抗旋转层ARConv来统一网络结构，降低模型训练时间，减少漏检；并提出P-NMS算法和限制函数优化训练样本，减少误检；在测试阶段，提出单张图片批量测试方法，有效提高模型召回率。实验结果表明，改进后算法具有更强的鲁棒性，并且能有效降低误检、漏检率提升网络性能。

关键词: 多目标检测, SSD算法优化, 抗旋转卷积层（ARConv）, 概率非极大值抑制（P-NMS）算法, 图片批量测试