Power Tools Classification Using FPGA

Power tools classification using FPGA presents a study aimed at automating the classification of power tools depicted in images into distinct categories such as drills, cutters, and chainsaws. Leveraging the ResNet50 architecture, we employ FPGA for efficient hardware acceleration. The study utilizes a dataset specifically curated for power tools classification. The experimental results substantiate the effectiveness of the proposed approach, achieving a classification accuracy that surpasses the benchmark on a test dataset. By employing FPGA, the proposed system offers advantages in terms of real-time processing and resource efficiency. This research adds value to computer vision by demonstrating a real-world application of FPGA-accelerated deep learning models within industrial environments.