The proposed framework is working on the edge computing architecture consisting of three entities: the mobile edge devices, remote edge server, and wireless AP. In mobile edge devices, the image **data** are captured using a camera module attached to the edge device. Subsequently, it is resized and normalized to fit the predefined input shape of the object detection DNN model. Then, the DNN inference, which can be executed using two methods, is performed to detect the location and classification of the objects included in the image. First, the edge devices are capable of offloading the DNN inference tasks to the edge server, which is called DNN offloading. Second, edge devices are capable of processing the DNN inference tasks under the assumption that the edge devices are equipped with a lightweight GPU hardware.