Hybrid CNN-Based Border Pixel Extraction with Attention-Enhanced Feature Fusion and Explainable AI for Real-Time Traffic Object Detection

This study presents a novel object recognition model, called Object Border Pixel Extraction with Precise Shape Detection using CNN (OBPE-PSD-CNN), to improve fine-grained object recognition in complicated traffic scenarios. In contrast to traditional deep learning-based object detection techniques that mainly use bounding boxes or coarse segmentation masks, the suggested one incorporates border pixel extraction and skeletal-contour-based shape modeling into the detection pipeline and allows the object to be represented precisely in terms of structure and geometry. In addition to enhancing discriminative ability in groups of visually similar objects, a hybrid deep feature fusion architecture based on ResNet-50 and AlexNet is proposed, and then an attention-enhanced classifier based on a Convolutional Block Attention Module (CBAM) is used to refine features adaptively, spatially, and channel-wise. Unlike the current literature where explainability is seen as a post-hoc analysis, the suggested framework entails Explainable AI (XAI) mechanisms in the architecture, offering visual pieces of evidence on model decisions in real-time. The suggested OBPE-PSD-CNN is an end-to-end solution to object detection, accurate shape extraction, segmentation, classification, and temporal tracking. Large-scale testing on benchmark traffic data such as CityFlow and UA-DETRAC demonstrates that the suggested algorithm is more effective than the state-of-the-art strategies, especially in dense, heterogeneous, and visually demanding traffic cases.