<![CDATA[This study evaluated YOLO11m-based vehicle counting in Pantau Semar CCTV recordings and analyzed how class-specific counting differences affect standardized CO₂ and CO estimates. The dataset comprised 12,000 images: 9,120 for training, 2,280 for validation, and 600 for testing. Model development used main training and corrective fine-tuning with Ultralytics. A Python pipeline combined ByteTrack, class stabilization, and line-crossing counting. Automated counts were compared with manual reference counts across six daytime and nighttime recordings from three locations. The detector achieved precision of 0.904, recall of 0.895, mAP₅₀ of 0.947, and mAP₅₀–₉₅ of 0.833. The system counted 7,444 vehicles compared with 7,667 manually, a difference of −2.91%; daytime and nighttime differences were +0.88% and −6.55%, respectively. Using identical emission factors and a one-kilometer reference distance, system-derived estimates were 1.70–3.88% lower for CO₂ and 2.79–4.94% lower for CO across six scenarios. The study shows how class-specific counting errors and emission factors produce emission-estimate differences that cannot be inferred from total-count differences alone. Findings are limited to the evaluated recordings and assumptions; the results are standardized comparative estimates rather than measurements of actual emissions.]]>
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