Super-Resolution GAN and Global Aware Object Detection System for Vehicle Detection in Complex Traffic Environments

Intelligent vehicle detection systems have the potential to improve road safety and optimize traffic management. Despite the continuous advancements in AI technology, the detection of different types of vehicles in complex traffic environments remains a persistent challenge. In this paper, an end-to-end solution is proposed. The image enhancement part proposes a super-resolution synthetic image GAN (SSIGAN) to improve detection of small, distant objects in low-resolution (LR) images. An edge enhancer (EE) and a hierarchical self-attention module (HS) are applied to address the loss of high-frequency edge information and texture details in the super-resolved images. The output super-resolution (SR) image is fed into detection part. In the detection part, we introduce a global context-aware network (GCAFormer) for accurate vehicle detection. GCAFormer utilizes a cascade transformer backbone (CT) that enables internal information interaction and generates multi-scale feature maps. This approach effectively addresses the challenge of varying vehicle scales, ensuring robust detection performance. We also built in a cross-scale aggregation feature (CSAF) module inside GCAFormer, which fuses low- and high-dimensional semantic information and provides multi-resolution feature maps as input to the detection head, so as to make the network more adaptable to complex traffic environments and realize accurate detection. In addition, we validate the effectiveness of our proposed method on a large number of datasets, reaching 89.12% mAP on the KITTI dataset, 90.62% on the IITM-hetra, 86.83% on the Pascal VOC and 93.33% on the BDD-100k. The results were compared to SOTA and demonstrated the competitive advantages of our proposed method for Vehicle Detection in complex traffic environments.