In crowded scenes, it is difficult for YOLOv3 to detect the objects that overlap each other heavily. Aiming at the reasons for the decline of YOLOv3 performance, three improvements are proposed. Firstly, a Tight Loss function is proposed, which optimizes the variance and mean of the coordinates of the prediction boxes to make the prediction boxes belonging to the same target more compact, thus reducing the false positive rate. Secondly, a high-resolution feature pyramid is proposed, in which the resolution of each pyramid feature is improved by upsampling, and shallow features are introduced to enhance the differences between adjacent sub-features, so as to generate distinguishing depth features for highly overlapped targets. Thirdly, a detection head based on spatial attention mechanism is proposed to reduce the number of redundant prediction boxes, so as to reduce the computational burden of the non-maximum suppression (NMS) process. The experimental results on the crowded dataset CrowdHuman show that the average accuracy and recall rate of YOLOv3 detection are improved by 2.91 percentage points and 3.20 percentage points, and the miss rate is reduced by 1.24 percentage points by using the proposed algorithms under the condition of using the traditional NMS method, which demonstrates the effectiveness of the proposed algorithms in boosting the performance in occluded pedestrian detection.