For an autonomous ship to navigate safely and avoid collisions with other ships, reliably detecting and classifying nearby ships under various maritime meteorological environments is essential. In this paper, a novel probabilistic ship detection and classification system based on deep learning is proposed. The proposed method aims to detect and classify nearby ships from a sequence of images. The method considers the confidence of a deep learning detector as a probability; the probabilities from the consecutive images are combined over time by Bayesian fusion. The proposed ship detection system involves three steps. In the first step, ships are detected in each image using Faster region-based convolutional neural network (Faster R-CNN). In the second step, the detected ships are gathered over time and the missed ships are recovered using the Intersection over Union of the bounding boxes between consecutive frames. In the third step, the probabilities from the Faster R-CNN are combined over time and the classes of the ships are determined by Bayesian fusion. To train and evaluate the proposed system, we collected thousands of ship images from Google image search and created our own ship dataset. The proposed method was tested with the collected videos and the mean average precision increased by 89.38 to 93.92% in experimental results.

Unlike high-end 3-dimensional (3D) scanners with more than 16 layers which are mainly used in academia, low-end 3D scanners with a few layers are being developed by sensor makers for installation in commercial advanced driver assistance system (ADAS). The output of a low-end 3D scanner is completely different from that of a full 3D scanner and it is rather similar to the output of a 2D scanner with a single layer. In this paper, a new framework for moving object detection and subsequent tracking using a low-end 3D scanner with 4 layers is proposed. The proposed method uses the contours of the objects to obtain a robust association between a detection and a tracking. The proposed method comprises five steps: preprocessing, contour extraction, hypothesis generation, pruning, and moving object detection. In the preprocessing step, outliers, such as the ground or backlights from preceding vehicles, are removed and the scanned points are decomposed into segments, each of which corresponds to a single object. In the track hypothesis generation step, each segment is associated with an existing track maintained over multiple scans. The association method developed here uses the contour shape of the segments and is motivated by the dynamic time warping. In the track hypothesis pruning step, unlikely tracks are removed from the hypothesis trees based on the proposed hypothesis scores. In the last step, moving objects are detected based on the track velocity. The proposed method is applied to four challenging real-world scenarios, and its validity is demonstrated via experimentation.

The major problem in an advanced driver assistance system (ADAS) is the proper use of sensor measurements and recognition of the surrounding environment. To this end, there are several types of sensors to consider, one of which is the laser scanner. In this paper, we propose a method to segment the measurement of the surrounding environment as obtained by a multi-layer laser scanner. In the segmentation, a full set of measurements is decomposed into several segments, each representing a single object. Sometimes a ghost is detected due to the ground or fog, and the ghost has to be eliminated to ensure the stability of the system. The proposed method is implemented on a real vehicle, and its performance is tested in a real-world environment. The experiments show that the proposed method demonstrates good performance in many real-life situations.

There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.