%0 Journal Article %A WANG Changqing %A HE Kunyu %A JIANG Shuai %T Narrow Space Object Detection Method by Improved YOLOv4-tiny Network %D 2022 %R 10.3778/j.issn.1002-8331.2112-0593 %J Computer Engineering and Applications %P 240-248 %V 58 %N 10 %X Aiming at the problems of a large number of missed detections and classification errors in the light detection network due to the mutual occlusion of objects in narrow spaces, an adaptive non-maximum suppression(A-NMS) multiscale detection method based on YOLOv4-tiny network is proposed. A large-scale feature map optimization approach and a pyramid pooling model are incorporated into the backbone network to enhance the major regional features of hidden objects. To improve the fusing of shallow detail data with high-level semantic information, a two-way pyramidal feature fusion network with embedded spatial attention is designed. A dynamic NMS threshold setting method that correlates the regional object density with the distance factor of the center of the bounding box is proposed and replaces the traditional IoU-NMS algorithm in the post-processing stage to further reduce the missed detection. The experimental results show that compared with the YOLOv4-tiny algorithm, the improved algorithm improves the mAP value by 2.84 percentage points and 3.06 percentage points on the public dataset PASCAL VOC07+12 and the self-made dataset, respectively, while the FPS remains at 87.9, the detection capability of occluded objects is significantly improved to meet the demand of mobile terminals for real-time detection of narrow and complex scenes. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2112-0593