- Publication Date:
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- IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)
- Sponsoring Org:
- National Science Foundation
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State-of-the-art lane detection methods use a variety of deep learning techniques for lane feature extraction and prediction, demonstrating better performance than conventional lane detectors. However, deep learning approaches are computationally demanding and often fail to meet real-time requirements of autonomous vehicles. This paper proposes a lane detection method using a light-weight convolutional neural network model as a feature extractor exploiting the potential of deep learning while meeting real-time needs. The developed model is trained with a dataset containing small image patches of dimension 16 × 64 pixels and a non-overlapping sliding window approach is employed to achieve fast inference. Then, the predictions are clustered and fitted with a polynomial to model the lane boundaries. The proposed method was tested on the KITTI and Caltech datasets and demonstrated an acceptable performance. We also integrated the detector into the localization and planning system of our autonomous vehicle and runs at 28 fps in a CPU on image resolution of 768 × 1024 meeting real-time requirements needed for self-driving cars.
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Identifying splice site regions is an important step in the genomic DNA sequencing pipelines of biomedical and pharmaceutical research. Within this research purview, efficient and accurate splice site detection is highly desirable, and a variety of computational models have been developed toward this end. Neural network architectures have recently been shown to outperform classical machine learning approaches for the task of splice site prediction. Despite these advances, there is still considerable potential for improvement, especially regarding model prediction accuracy, and error rate.
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Our five-fold cross validation ensured the prediction accuracy of our models are consistent. For reproducibility, all the datasets used, models generated, and results in our work are publicly available in our GitHub repository here:
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