This thesis focuses on the hardware implementation of an object detection system, in the context of a larger project to create an autonomous robot for grape harvesting equipped with advanced visual recognition capabilities. The primary objective of this work is to deploy a custom object detection algorithm capable of identifying bunches of grapes and to design a specialized hardware architecture capable of making it run fast and at low cost within the context of edge computing. The object detection task is usually entrusted to a general-purpose processor, which is simple to program but expensive and has improvable performance. To improve performance and decrease cost, this paper proposes a hardware acceleration Field Programmable Gate Array (FPGA) architecture for a custom YOLOv3-Tiny algorithm, which is a state-of-the-art, real-time object detection system based on a Convolutional Neural Network (CNN) architecture.