## 5 Results and Discussion  
In this section, we present our results that solve the two optimization problems \"manually\" by finding the optimal values of the decision variables \(a_{i}\) and \(q\). We define for i = 1 the edge TPU inference machine, i = 2 the TensorFlow-Lite inference machine with 224 \(\times\) 224 \(\times\) 3 NN input size, and i = 3 the TensorFlow-Lite inference machine with 320 \(\times\) 320 \(\times\) 3 NN input size. \(T_{min}\) is the target execution time requested by the user. Figure 10: Accuracy values for every device and batch size. Figure 9: E2E execution time values for every device and batch size. In Figure 11, we observe that the Pareto plot depicts the most efficient solutions for our two optimization problems. Every solution in the Pareto plot combines three \(a_{i}\) values. Regarding the optimal value for the encoding rate, it is clearly shown in Figures 6-8 that the optimal value for the encoding rate is \(q=50\). To answer \(Q1\) according to the Pareto, the optimal solution to get the minimum E2E execution time is \(T_{total}=110\) s. The most optimal values for \(a_{i}\) are \(a_{1}=0.7\), \(a_{2}=0.3\), \(a_{3}=0\). For \(Q2\), we define it as \(T_{min}=400\) (s), which means that all the feasible solutions can not be greater than 400 s. According also to the Pareto plot, the most optimal solution for \(Q2\) achieves \(S_{acc}=0.53\) and \(T_{total}=342\) s. The values for \(a_{i}\) which achieve the maximum accuracy \(a_{1}=0\), \(a_{2}=0.8\), \(a_{3}=0.2\). The main idea of this paper was to develop a cooperative task execution of an edge-assisted object detection system. In our edge computing system, we used the You Only Look Once (YOLO) version 5. We first evaluate the performance of the YOLOv5 on every end device using metrics like Precision, Recall, and mAP on the COCO dataset. The goal of our work was to offload the workload (batch of images) to three end devices that are in our testbed and try to get the minimum execution time or the maximum accuracy. We noticed that the parameter-decision variables, which affect the E2E execution time, and execution accuracy are: the encoding rate \(q\) (compression value); and the batch sizes (splitting decisions) sent to every device. More specifically, we explored the optimal trade-offs between the parameters and decision variables to solve optimization problems \"manually\". We found that the key parameter which affects an edge-assisted object detection system is the encoding rate (compression value) along with the neural network input size and the precision (INT8, float32) and also the batch sizes (splitting decisions) sent to every end device. Actually, the effectiveness of image compression on object recognition accuracy has recently gained much attention from many researchers. The authors in [37] showed the significance of the effectiveness of image compression or better encoding rate \(q\) on the object detection accuracy. The system-level trade-offs between E2E latency and deep learning accuracy were recently introduced in [16]. We tried to learn from these lessons and add value to the relevant literature. Figure 11: Most best solutions (Pareto front) for \(Q1\), \(Q2\). The contribution of our work is that we propose an edge-assisted object detection system that offloads the workload from the edge server to the end devices based on the needs of the user rather than offloading the workload from the end devices to the edge server and the cloud [32; 37]. This approach has not been previously explored. In our work, we highlight the optimal trade-offs between E2E execution time and accuracy in an edge computing architecture that other researchers have also studied. Our work, however, goes beyond that by providing a real-time edge-assisted object detection system that is tailored made according to the needs of the user by offloading the workload to the three devices. Our proposed offloading strategies can easily be implemented on end devices, which means that the latency of our system can be reduced much more.