🤖 AI Summary
This work addresses the challenge of achieving real-time performance, high accuracy, and energy efficiency in embedded vision systems operating on resource-constrained hardware. The authors propose an algorithm-hardware co-design methodology tailored for DSP/FPGA platforms, optimizing edge, corner, and blob detection operators through hardware-aware algorithmic refinements and quantization techniques. To further enhance throughput without compromising image quality, the approach incorporates inter-frame redundancy elimination and adaptive frame averaging strategies. Experimental results demonstrate that, compared to conventional solutions, the proposed method delivers significantly improved processing speed and energy efficiency, enabling scalable and highly effective real-time embedded vision across diverse applications such as automotive systems, surveillance, and robotics.
📝 Abstract
Embedded vision systems need efficient and robust image processing algorithms to perform real-time, with resource-constrained hardware. This research investigates image processing algorithms, specifically edge detection, corner detection, and blob detection, that are implemented on embedded processors, including DSPs and FPGAs. To address latency, accuracy and power consumption noted in the image processing literature, optimized algorithm architectures and quantization techniques are employed. In addition, optimal techniques for inter-frame redundancy removal and adaptive frame averaging are used to improve throughput with reasonable image quality. Simulations and hardware trials of the proposed approaches show marked improvements in the speed and energy efficiency of processing as compared to conventional implementations. The advances of this research facilitate a path for scalable and inexpensive embedded imaging systems for the automotive, surveillance, and robotics sectors, and underscore the benefit of co-designing algorithms and hardware architectures for practical real-time embedded vision applications.