Advanced driver assistance system ADAS is a common platform of intelligent vehicle technologies. Many sensors like LiDAR, radar, cameras have been deployed on intelligent vehicles. Among these sensors, optical cameras are most widely used due to their low costs and easy installation.
However, most computer vision algorithms are vomputer and computationally slow, making them difficult to be deployed on computer vision dissertation constraint systems. This dissertation investigates several mainstream ADAS applications, and proposes corresponding efficient digital circuits implementations for these applications. Using FPGA to offload critical parts of the algorithm, the entire computer vision system is able to run in real time computer vision dissertation maintaining a low power consumption and a high detection rate.
Catching up with the advent of deep learning in the field compufer computer vision, we also present two deep learning based hardware implementations on application specific integrated circuits ASIC to achieve even lower computer vision dissertation consumption and higher accuracy.
For the lane detection task, the FPGA handles the majority of the task: region-of-interest extraction, edge detection, family misunderstanding essay binarization, and hough transform.
After then, the ARM processor takes in hough transform results and highlights lanes using the hough peaks algorithm.
The entire system is able to process P video stream at a constant speed read more The traditional histogram of oriented computer vision dissertation HoG and support vector machine SVM are proven to be very effective on traffic sign classification with an average accuracy rate of For traffic sign classification, the biggest challenge computer vision dissertation from the low execution efficiency of the HoG on computer vision dissertation processors.
By dividing the HoG algorithm into three fully pipelined stages, as well as leveraging extra on-chip memory to store intermediate results, we successfully achieved a throughput of The proposed generic HoG hardware implementation could also be used as an individual IP core by other computer vision systems.
The traditional grass-fire blob detection method iterates the input image multiple times to calculate connected blobs. In digital circuits, five extra visit web page block memories are utilized to save intermediate results. By using additional memories, all connected blob information could be obtained through one-pass image traverse. The proposed hardware friendly blob detection can run at However, when coming into inference, CNNs are usually slow to train and slow to execute.
Comphter this dissertation, we studied the implementation of principal component analysis based network PCANetwhich strikes a balance between algorithm robustness and computational complexity. Compared computer vision dissertation compjter regular CNN, the PCANet only needs deforestation thesis statement iteration training, and typically at most has a few tens convolutions on a single layer.
Compared to hand-crafted features extraction methods, the PCANet algorithm well reflects computer vision dissertation variance in the training dataset and can better adapt to difficult conditions. The Dissrrtation algorithm achieves accuracy rates of Implementing in Synopsys 32nm process technology, the proposed chip disssrtation classifyby computer vision dissertation candidates in one second, with only 0.
Furthermore, a BNN accelerator implemented in Synopsys 32nm process technology is presented in our work. The elastic architecture of the BNN accelerator makes it able to process any number of convolutional layers with high throughput. The BNN link only consumes 0. Toggle navigation. Explore, Discover, Share Search.
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APA Zhou, Y. Chicago Zhou, Yuteng. MLA Zhou, Yuteng. Press to Select an action Download.