关键词: 深度学习, 计算机视觉, SSD, 安全帽检测, 特征融合, 小目标

Abstract:

Safety helmets worn by workers is an important part of safety construction, to protect workers’ lives and overcome the defect of manual inspection, a new helmet detection method based on improved Single Shot MultiBox Detector（SSD） is proposed. In consideration of objects’ distribution in helmet dataset is imbalanced and objects are generally small, a branch network for feature fusion is added to SSD, it can enhance the shallow feature maps’ semantics and the mAP（mean Average Precision） of SSD300 is increased by 2.3 percentage points, and the real-time detection rate is reduced by only 1.3 frame/s, reaches 39.6 frame/s. In order to make SSD’s prior boxes match with the effective receptive field, the default box setting method is improved and the size of prior boxes is adjusted indirectly by introducing a variable parameter. The mAP of SSD300 and SSD512 reach 74.6% and 82.5% respectively. The experimental results show that the improved SSD model has excellent accuracy and good real-time performance and basically meets the requirements of practical application.

Key words: deep learning, computer vision, Single Shot MultiBox Detector（SSD）, helmet detection, feature fusion, small object