In this paper, we present a novel model to detect lane regions and extract lane departure events (changes and incursions) from challenging, lower-resolution videos recorded with mobile cameras. Our algorithm used a Mask-RCNN based lane detection model as pre-processor. Recently, deep learning-based models provide state-of-the-art technology for object detection combined with segmentation. Among the several deep learning architectures, convolutional neural networks (CNNs) outperformed other machine learning models, especially for region proposal and object detection tasks. Recent development in object detection has been driven by the success of region proposal methods and region-based CNNs (R-CNNs). Our algorithm utilizes lane segmentation mask for detection and Fix-lag Kalman filter for tracking, rather than the usual approach of detecting lane lines from single video frames. The algorithm permits detection of driver lane departures into left or right lanes from continuous lane detections. Preliminary results show promise for robust detection of lane departure events. The overall sensitivity for lane departure events on our custom test dataset is 81.81 .
Neural Networks have been a subject of investigation in the autonomous vehicles field for a while. Among the very first attempts to use a neural network for vehicle navigation, ALNINN @cite_12 is considered a pioneer and one of the most influential paper. This model is comprised of a shallow neural network that predicts actions out of captured images from a forward facing camera mounted on-board a vehicle, with few obstacles, leading to the potential use of neural networks for autonomous navigation. More recently, advances in object detection such as the contribution made by DL and Region Convolutional Neural Network (R-CNN) @cite_14 in combination with Region Proposal Network (RPN) @cite_4 have created models such as Mask R-CNN @cite_1 that provide state of the art predictions. New trends in Neural Network object detection include segmentation, which we applied in our model as an estimator for LDW.
@cite_1 @cite_14 @cite_4 @cite_12
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State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps ( including all steps ) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine large graphical models with low-level vision have been proposed to address this problem; however, we propose an end-to-end recurrent neural network (RNN) architecture with an attention mechanism to model a human-like counting process, and produce detailed instance segmentations. The network is jointly trained to sequentially produce regions of interest as well as a dominant object segmentation within each region. The proposed model achieves state-of-the-art results on the CVPPP leaf segmentation dataset and KITTI vehicle segmentation dataset. Since the introduction of deep convolutional neural networks (CNNs), object detection in imagery has witnessed substantial breakthroughs in state-of-the-art performance. The defense community utilizes overhead image sensors that acquire large field-of-view aerial imagery in various bands of the electromagnetic spectrum, which is then exploited for various applications, including the detection and localization of man-made objects. In this work, we utilize a recent state-of-the art object detection algorithm, faster R-CNN, to train a deep CNN for vehicle detection in multimodal imagery. We utilize the vehicle detection in aerial imagery (VEDAI) dataset, which contains overhead imagery that is representative of an ISR setting. Our contribution includes modification of key parameters in the faster R-CNN algorithm for this setting where the objects of interest are spatially small, occupying less than 1:5×10-3 of the total image pixels. Our experiments show that (1) an appropriately trained deep CNN leads to average precision rates above 93 on vehicle detection, and (2) transfer learning between imagery modalities is possible, yielding average precision rates above 90 in the absence of fine-tuning.