POD-YOLO Object Detection Model Based on Bi-directional Dynamic Cross-level Pyramid Network

The existing heavy-backbone object detection models overlook the crucial role of cross-level interactive fusion of feature information in pyramid networks, resulting in the inability to detect occluded objects or small objects in complex scenes. In this thesis, we present a new heavy-neck object detection model called POD-YOLO based on YOLOv5s. Firstly, we propose the POD-RepC3 module to increase the model's capability to obtain the multi-layer feature. Additionally, addressing the issue of large object size span, we propose a bidirectional partial dynamic fusion module (Bi-PDC) as the detection neck of the pyramid network. This module preserves the accurate positioning signals and facilitates cross-level interactive fusion of feature information. Finally, we design Reparameterized Bi-directional Dynamic Feature Pyramid Network (RepBi-DFPN), a deep feature fusion network that integrates contextual information and enhances both feature expression and fusion capabilities of our model. The experiment results suggest that the suggested method is positive on the PASCAL VOC dataset. The mAP@0.5 and mAP@0.5 :0.95 performance reached 81.3% and 58.2%, respectively, which increased by 2.4% and 4.1% compared to original algorithm YOLOv5s. Furthermore, experiment results also demonstrate that model's performance can compete with SOTA object detection models. In this paper, the algorithm optimizes the feature fusion capability of the pyramid network to effectively decrease the false detection and missing detection of the model. The model's ability to accurately detect multi-scale targets is significantly improved.