## 1 Introduction  
The swift advancement of computing resources and storage of the Internet of Things devices has made significant progress in a wide range of applications, leading to an era in which computing and processing of data are flowing from the cloud to the edge [1, 2]. Many cloud-based applications that were executed previously in the cloud are now delivered by edge devices such as tablets, wearable IoT devices, or smartphones [3]. According to recent literature [4, 5] the number of connected devices is expected to reach 150 billion by the year 2025. Moreover, almost 70% of the computing workloads will be executed at the edge of the network in the next years. The concept of edge computing involves performing data processing at the edge devices, resulting in improving the performance of the applications by using an edge server which provides many benefits like reducing computational latency and energy consumption. A great effort has been made by many researchers to develop different optimization tools to minimize energy consumption and latency and to maximize the performance of edge computing applications. In addition, Artificial Intelligence, defined as a powerful vehicle for the optimization of machines' interpretation, learning, and task-performing capacity [6], has already expanded in a wide range of applications, including reinforcement learning and computer vision. Furthermore, real-time applications like autopilot-assisted driving and, in a few years, connected and autonomous vehicles depend on the fast processing of incoming data, which makes Cloud AI not the best solution. Edge AI is a promising solution to face these problems [7; 8]. This solution accommodates not only the inference but also the training of the AI models to the edge. Avoiding the transmission of the data to the cloud mitigates latency, privacy, and traffic load problems. The training of a deep learning model requires intensive computing resources. Nonetheless, the explosive evolution of the hardware and the processing power of the edge devices can provide these intensive computing resources which are required to develop an AI application at the edge [9; 10]. The core concept behind edge computing is to provide Cloud-like functionalities closer to the end device and the user. In other words, a Cloud-like device should be capable of transferring (offloading) the computationally-intensive task to a close-edge device, to reduce, for example, latency and power consumption. This approach is also called task offloading, which allows lower latency, improved energy consumption, and much better reliability. Task offloading has gained much attention from the academic community and the industry. Many studies were published in recent years addressing task offloading, for example, [PERSON] et al. in 2017 and [PERSON] et al. in 2018 made an effort to classify the various types of task offloading. The aspects on which this body of literature is particularly focused are architecture, resource allocation, communications, algorithms, etc. [11; 12; 13; 14]. In this paper, we study a cooperative task execution of an edge-assisted object detection system. In more detail, we chose to use the official pre-trained YOLOv5 [15] object detector using the COCO dataset as workload. This was offloaded to every end device to get the object detector evaluation metrics like mAP, precision, recall, and also execution time, and other metrics for every end device. We used the YOLO object detector and the COCO dataset because they can bench-mark the performance of real-time object detection systems. Moreover, we study two natural criteria for selecting how to compress the images and how to split the image set across the available end devices. We searched experimentally for the optimal trade-off among these metrics to provide optimization in terms of execution accuracy and execution time. In our testbed, we highlight optimal trade-offs between the E2E execution time and execution accuracy of our object detection system.