<![CDATA[Motorcycles are the dominant transportation mode in developing nations, yet their high volume exacerbates traffic violations and accidents. Automated identification of motorcycle operators is crucial for Intelligent Transportation Systems (ITS). However, existing advanced vision models, such as the Segment Anything Model (SAM) or YOLOv9, demand substantial computational resources, rendering them impractical for real-time edge deployment. Furthermore, standard object detectors often fail to contextually distinguish active riders from bystanders. To address these limitations, this study proposes a lightweight, context-aware hybrid framework integrating YOLOv5 for rapid spatial detection and SqueezeNet for semantic contextual verification. The core novelty lies in the Crop-Union Context Verification Mechanism, which extracts the spatial union of paired person-motorcycle bounding boxes, expands it proportionally, and classifies the cropped region using SqueezeNet to confirm the presence of an active operator. Additionally, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied during preprocessing to enhance image quality under varying illumination. Experimental results demonstrate that the hybrid system successfully identifies motorcycle operators with high spatial confidence (YOLOv5: 0.84 for person, 0.86 for motorcycle) and robust contextual validation (SqueezeNet Top-1 prediction: moped, probability 0.247). By utilizing SqueezeNet’s highly efficient Fire Module architecture (~1.2 million parameters), the proposed framework achieves exceptional computational efficiency without sacrificing discriminative power. Ultimately, the disjunctive fusion of spatial and semantic signals ensures system resilience. This lightweight pipeline proves highly viable for real-time, AI-driven traffic surveillance on resource-constrained edge devices, offering a scalable solution for smart city infrastructure and automated law enforcement.]]>
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