Optimization of Deep-Learning Detection of Humans in Marine Environment on Edge Devices

Artificial intelligence (AI) detection techniques based on convolution neural networks (CNNs) require high computations and memory. Their deployment on embedded edge devices, with reduced resources and power budget, is highly hindered especially for applications that requires real-time inference. Several optimization methods such as pruning, quantization and using shallow networks, are mainly utilized to overcome this limitation but at the cost of degradation in detection performance. However, efficient approaches for training and inference have been recently introduced to lower such degradation. This work investigates the use of these approaches to optimize the popular You Only Look Once (YOLO) network targeting various emerging edge devices (Nvidia Jetson Xavier AGX, AMD-Xilinx Kria KV260 Vision AI Kit, and Movidius Myriad X VPU) in order to enhance the detection of humans in maritime environment.