Design of a Hardware-Optimized High-Performance CNN Accelerator for Real-Time Object Detection Using YOLOv3 with Darknet-19 Architecture

This research proposes a novel hardware-optimized design to accelerate Convolutional Neural Networks (CNNs) using Verilog HDL. The design is specifically developed for the DARKNET-19 system model, which serves as the backbone of the YOLOv3-tiny algorithm, a widely used framework for real-time object detection in dynamic environments. The CNN architecture was implemented in Verilog HDL and synthesized using Synopsys Design Compiler, with a focus on improving both object detection accuracy and hardware resource efficiency. The proposed design efficiently performs key CNN operations, including convolution, pooling, and activation, enabling faster real-time object detection compared to many existing methods. To improve performance, the hardware design incorporates parallel processing techniques, allowing multiple computations to be executed simultaneously. This significantly reduces the system latency and power consumption. The convolutional layers of the DARKNET-19 architecture are efficiently mapped onto the hardware platform, ensuring optimized data storage and fast memory access, which further enhances processing speed and detection accuracy. An innovative feature of the design is a 2-dimensional image preprocessing module that prepares input images before they are fed into the CNN. This preprocessing stage includes image resizing, brightness normalization, and color adjustment, which helps the CNN process visual data more effectively. After preprocessing, the images pass through several CNN layers. The convolutional layers extract key features from the images, while the pooling and activation layers refine these features to improve detection performance. Finally, the processed data is analyzed by the YOLOv3-tiny algorithm, which identifies and locates objects in the images with high precision. Experimental results demonstrate that the proposed high-speed and resource-efficient hardware architecture is well-suited for real-time object detection applications, particularly in highly dynamic and unpredictable environments.