This paper investigates the latency challenges in field-edge systems within transport monitoring environments, with a focus on collision detection and object tracking. Leveraging a field-edge network architecture, we explore the integration of image preprocessing at the field device level and complex object detection and tracking models at the edge device level. Our approach is focused on optimizing the balance between processing speed and accuracy, addressing the critical need for real-time responsiveness in dynamic scenarios such as smart city infrastructure and autonomous navigation. We build on the foundation of existing object detection algorithms, i.e. YOLO, and introduce a batch processing strategy with parallel threads to mitigate latency issues inherent in sequential processing frameworks. Our system is tested using a YOLOv8 model trained on a custom Traffic Object Detection dataset, demonstrating the ability to maintain high processing speeds, with performance exceeding 30 frames per second, without sacrificing accuracy. The approach combines advanced object detection and tracking algorithms with network optimization. Our system tests show positive results in reducing latency. This work sets a benchmark for the deployment of field-edge systems in transport monitoring and contributes to future advancements in smart transportation and autonomous systems.