Published Paper Details:

An object detection processor based on a binarized neural network (BNN) architecture is developed to enable efficient deployment on resource-constrained FPGA platforms. Traditional convolutional neural networks (CNNs), while highly effective for visual recognition tasks, impose significant demands on computational resources and memory bandwidth, present challenges for embedded applications. To address these limitations, the proposed system leverages binary operations, on-chip memory usage, and an optimized neural architecture to minimize complexity and power consumption. The design achieves accurate object detection performance within the limitations of FPGA hardware, highlighting its potential for efficient inference in real-time environments. Its modularity and scalability allow for adaptation across a wide range of embedded vision scenarios, particularly in domains requiring low latency and high energy efficiency. The proposed approach demonstrates the feasibility of integrating deep learning models into compact, resource-effective hardware systems, offering a promising direction for future applications in areas such as IoT monitoring, smart sensors, and mobile robotics.