The safety of vehicle driving has always been a concern of people. Vehicle-mounted pedestrian blind spot detection systems can effectively reduce the risk of pedestrian collisions. The existing in-vehicle blind spot pedestrian detection solutions often suffer from high power consumption and costs, while cloud-based processing solutions struggle to meet the latency-sensitive requirements of vehicle-side detection. To address these issues, this paper employs a low-power and low-cost edge computing platform K230, combined with the YOLOv8 algorithm, and completes training using the WiderPerson pedestrian dataset. Finally, a camera-based intelligent vehicle blind spot pedestrian detection system is implemented through quantization deployment. Experimental results demonstrate that the system can accurately identify pedestrian targets and issue safety warnings even in scenarios with dense road pedestrians and occlusions.