HDPNet: A Hybrid Dynamic Perception Network for Robust Object Detection in Low-Light and Deformed Environments
Qiaochu Li, Yingnan Zhou, Junyuan Zhang, Lingfei Xu, Juelin Chen, Zhengzhou Li · Applied Sciences · 2025
Achieving robust visual detection under challenging conditions such as poor illumination and target deformation remains a critical challenge for computer vision systems. Although the YOLO series of object detection algorithms excel in speed and accuracy, their performance significantly degrades under non-ideal lighting conditions. To address this issue, we propose a Hybrid Dynamic Perception Network (HDPNet), a framework specifically designed for high-precision, real-time object detection in harsh environments. HDPNet integrates three core modules into the YOLOv8n architecture to form a hybrid structure. The Dynamic Illumination-aware Module (DIM) adaptively enhances features under varying illumination through global encoding and a dual-attention mechanism, the Interactive Attention Fusion Network (IAFN) optimizes cross-modal features using a lightweight Transformer-CNN interactive architecture, and the Multi-branch Decomposition Network (MDN) captures multi-scale deformation features by combining deformable convolution and sparse Transformer. Experimental results on the self-built low-light industrial express package dataset named njpackage show that our method achieves an [email protected] of 86.6%, which is 4.4% higher than the baseline YOLOv8n model, while maintaining real-time inference speed of ≥45 FPS. The proposed HDPNet not only provides an effective solution for logistics automation but also offers a robust and versatile hybrid technical framework adaptable to other vision tasks facing similar challenges.