<![CDATA[A major challenge in the development of object detection technology is the significant reliance on large labeled datasets, which requires substantial time and memory for manual annotation—especially in complex, mixed traffic environments with varied vehicle types, congestion levels, and unpredictable motion patterns. This study addresses this issue by integrating the semi-supervised learning technique, FixMatch, into the YOLOv7 object detection model, utilizing 4000 transportation-related datasets. The FixMatch technique enables the model to detect unlabeled objects effectively through strong and weak augmentation methods. In this study, the detected objects in the mixed traffic environment include public transportation, pedicabs, cars, motorcycles, and trucks. This study achieved an impressive 97.5% detection accuracy by leveraging unlabeled data, demonstrating the model's efficiency and effectiveness in identifying vehicles under diverse traffic conditions. Consequently, integrating the FixMatch method into YOLOv7 provides a practical and efficient solution for object detection in situations where collecting labeled data is challenging, such as in dynamic and highly variable traffic environments.]]>
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