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Recently, the rapid development of convolutional neural networks (CNN) has consistently improved object detection performance using CNN and has naturally been implemented in autonomous driving due to its operational potential in real-time. Detecting moving targets to realize autonomous driving is an essential task for the safety of drivers and pedestrians, and CNN-based moving target detectors have shown stable performance in fair weather. However, there is a considerable drop in detection performance during poor weather conditions like hazy or foggy situations due to particles in the atmosphere. To ensure stable moving object detection, an image restoration process with haze removal must be accompanied. Therefore, this paper proposes an image dehazing network that estimates the current weather conditions and removes haze using the haze level to improve the detection performance under poor weather conditions due to haze and low visibility. Combined with the thermal image, the restored image is assigned to the two You Only Look Once (YOLO) object detectors, respectively, which detect moving targets independently and improve object detection performance using late fusion. The proposed model showed improved dehazing performance compared with the existing image dehazing models and has proved that images taken under foggy conditions, the poorest weather for autonomous driving, can be restored to normal images. Through the fusion of the RGB image restored by the proposed image dehazing network with thermal images, the proposed model improved the detection accuracy by up to 22% or above in a dense haze environment like fog compared with models using existing image dehazing techniques. [ABSTRACT FROM AUTHOR]

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird's eye view (BEV) representations. This structure combines independent detection results obtained in parallel through "you only look once" networks using an RGB image and a height map converted from the BEV representations of LiDAR's point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model. [ABSTRACT FROM AUTHOR]

Recently, as the demand for technological advancement in the field of autonomous driving and smart video surveillance is gradually increasing, considerable progress in multi-object tracking using deep neural networks has been achieved, and its application field is also expanding. However, various problems have not been fully addressed owing to the inherent limitations in video cameras, such as the tracking of objects in an occluded environment. Therefore, in this study, we propose a density-based object tracking technique redesigned based on DBSCAN, which has high robustness against noise and is excellent for nonlinear clustering. Moreover, it improves the noise vulnerability inherent to multi-object tracking, reduces the difficulty of trajectory separation, and facilitates real-time processing through simple structural expansion. Through performance test evaluation, it was confirmed that by using the proposed technique, several performance indices were improved compared to the existing tracking technique. In particular, when added as a post processor to the existing tracker, the tracking performance owing to noise suppression was considerably improved by more than 10%. Thus, the proposed method can be applied in industrial environments, such as real pedestrian analysis and surveillance security systems. [ABSTRACT FROM AUTHOR]

An essential component for the autonomous flight or air-to-ground surveillance of a UAV is an object detection device. It must possess a high detection accuracy and requires real-time data processing to be employed for various tasks such as search and rescue, object tracking and disaster analysis. With the recent advancements in multimodal data-based object detection architectures, autonomous driving technology has significantly improved, and the latest algorithm has achieved an average precision of up to 96%. However, these remarkable advances may be unsuitable for the image processing of UAV aerial data directly onboard for object detection because of the following major problems: (1) Objects in aerial views generally have a smaller size than in an image and they are uneven and sparsely distributed throughout an image; (2) Objects are exposed to various environmental changes, such as occlusion and background interference; and (3) The payload weight of a UAV is limited. Thus, we propose employing a new real-time onboard object detection architecture, an RGB aerial image and a point cloud data (PCD) depth map image network (RGDiNet). A faster region-based convolutional neural network was used as the baseline detection network and an RGD, an integration of the RGB aerial image and the depth map reconstructed by the light detection and ranging PCD, was utilized as an input for computational efficiency. Performance tests and evaluation of the proposed RGDiNet were conducted under various operating conditions using hand-labeled aerial datasets. Consequently, it was shown that the proposed method has a superior performance for the detection of vehicles and pedestrians than conventional vision-based methods. [ABSTRACT FROM AUTHOR]