Lightweight Real-Time Object Detection via Enhanced Global Perception and Intra-Layer Interaction for Complex Traffic Scenarios

Due to unfavorable factors such as cluttered spatial and temporal distribution of multiple types of targets, occlusion of background objects of different shapes, and blurring of feature information by inclement weather, the low detection accuracy in complex traffic scenarios has been a troubling issue. Regarding the above-mentioned issues, the paper proposes a lightweight real-time detection network to augment multi-scale object perception capabilities in traffic scenarios while ensuring real-time detection speed. First, we construct a novel global feature extraction (GFE) structure by cascading orthogonal band convolution kernels that capture the global dependencies between pixels to improve feature discrimination. Then, an intra-layer multi-scale feature interaction (IMFI) module is proposed to reinforce the effective reuse and multi-level transfer of salient features. In addition, we build a multi-branch scale-aware aggregation (MSA) module that captures abundant context-associated features to improve the target decision-making capability and the self-adaptive capability of the model when dealing with diverse object scales. Experimental results demonstrate that the proposed approach attains a significant improvement of 5.6 percentage points in AP50 with fewer parameters and computational power compared to the baseline model, with an improved FPS of 73. Furthermore, our approach strikes the optimal speed-accuracy balance when compared against other excellent object detection algorithms of the same magnitude.