Global road traffic accidents remain a significant problem resulting in large numbers of fatalities and economic costs. Automatic accident detection from traffic surveillance cameras can enable rapid emergency response to save lives and reduce traffic disruptions. In this paper we present our new deep learning architecture for the detection of road accidents from CCTV footage, called Enhanced Spatial Accident Detection Network (ESADN). In our approach we introduce three innovative concepts: a Transformer-inspired Spatial Attention Mechanism for modelling contextual relationships between image regions, a Feature Enhancement Module (FEM) that enhances the spatial features of accidents, and a Context Aware Classification Head for improving classification accuracy through feature fusion. The superiority of ESADN is demonstrated by extensive experiments on the Road Accidents from CCTV Footages Dataset (6,191 accident vs. 15,420 non-accident images): it achieves 99 % accuracy, 0.98 F1-score on the accident class, ROC-AUC = PR-AUC = 1.00, and an average precision of 1.00. When exported to ONNX and run with ONNX Runtime, the model runs at \(\sim \) 6.6 FPS (0.152 s/frame) and has a 105 MB footprint. Visualization of activation maps confirms that ESADN consistently focuses on collision zones and anomalous vehicle positions. ESADN’s high accuracy and edge-readiness make it an ideal candidate for real-world traffic monitoring systems.