Deep learning models have achieved significant success in a range of computer vision tasks and object detection, particularly in safety and security applications. However, deploying these models on edge devices, presents unique challenges due to limited computational resources, particularly for those cost-effective edge devices without GPU chips, such as Raspberry Pi 4. This work designed a lightweight object recognition model that can be used on edge computing devices with or without a GPU chip, enabling object recognition functionality. Compared to directly running YOLOv7-tiny model on the Raspberry Pi 4, the speed increased by 14 times.