With the advances of deep neural networks, there is progress on the detection and recognition of traffic lights for advanced driver assistance systems (ADAS). However, existing approaches most rely on the identification of traffic light boxes, followed by the recognition of signal lights. It is considered as a major drawback since light bulbs can be arranged in different directions or irregular patterns in different geographic regions. In this paper, we present a traffic light detection method based on direct recognition of individual signal lights. Our two-stage technique utilizes data augmentation and ensemble learning to detect the light bulbs with least miss rate. By learning the color characteristics from validation sets for data augmentation, it is able to achieve a signal light candidate detection rate at 97.26%. Followed by the classification stage, the recognition accuracy is given by 98.6%, which outperforms state-of-the-art traffic light detection algorithms. The source code and dataset are available at https://github.com/981124/yolov7 traffic light detect.

3D-HEVC (The 3D Extension of High Efficiency Video Coding) is the newest 3D video coding standard, which enriches multimedia applications with the video format of multi-view plus depth. For the depth map coding in 3D-HEVC, the advanced coding tools enhance the coding efficiency of the depth map and the quality of the synthesized view. However, the time consumption and complexity of 3D-HEVC also increase significantly. This paper utilizes the characteristics of human visual system to propose a fast algorithm based on visual perception for the acceleration of the depth intra coding of 3D-HEVC. The depth map is segmented into different regions by Otsu's auto-thresholding. The dominate edge direction is categorized for each prediction unit. We detect the perceptual edge based on just noticeable depth difference model to extract the area that may affect the visual perception. According to depth map segmentation and edge distribution, we reduce the corresponding intra angular modes and determine whether to perform depth modelling mode. We also incorporate the boundary continuity and rate-distortion cost thresholding to propose the fast coding unit decision. The experimental results show that the proposed algorithm eliminates 53.09% of the depth coding time with only 0.15% BD-BR on average. The coding performance of the proposed algorithm outperforms the previous works significantly.