Field-programmable gate arrays (FPGAS) have emerged as a powerful platform for real-time image processing due to their inherent parallelism and configurability. This paper presents an optimized hardware implementation of fundamental image processing algorithms including Sobel edge detection, Thresholding contrast stret...
This paper presents a resource-constrained, real-time on-board FPGA-based system architecture and design approach for unmanned aerial vehicle (UAV) computing hardware tailored for infrared small-target detection. Modern autonomous UAV systems require the high-throughput processing of video streams, demanding real-time...
Real-time image segmentation is essential for edge-based intelligent systems. Deep learning models are efficient for image segmentation compared to conventional techniques. Among other deep neural networks (DNN), MobileNet convolution neural network (CNN) model utilizes depthwise separable convolutions to reduce comput...
This paper presents a hardware-efficient object detection accelerator based on XNOR-driven variable-precision computation for real-time edge artificial intelligence. The proposed network combines DenseToRes and transition layers to preserve feature information under aggressive quantization. Binary convolution is execut...
Edge computing in electronic and information systems requires object detection algorithms that balance high accuracy with low computational complexity ${ }^{[1]}$. Traditional deep convolutional neural networks (CNNs) often fail to meet real-time processing constraints on resource-constrained embedded platforms due to...
Image processing is vital in modern embedded system, Applications such surveillance, medical imaging, industrial inspection and autonomous navigation requires image enhancement technique which improves visibility, feature detection and accuracy. This project presents a high-speed pipelined Field programmable gate array...
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