Overcoming Occlusion in the Automotive Environment—A Review

When performing multiple target detection, it is difficult to detect small and occluded targets in complex traffic scenes. To this end, an improved YOLOv4 detection method is proposed in this work. Firstly, the network structure of the original YOLOv4 is adjusted, and the 4× down-sampling feature map of the backbone network is introduced into the neck network of the YOLOv4 model to splice the feature map with 8× down-sampling to form a four-scale detection structure, which enhances the fusion of deep and shallow semantics information of the feature map to improve the detection accuracy of small targets. Then, the convolutional block attention module (CBAM) is added to the model neck network to enhance the learning ability for features in space and on channels. Lastly, the detection rate of the occluded target is improved by using the soft non-maximum suppression (Soft-NMS) algorithm based on the distance intersection over union (DIoU) to avoid deleting the bounding boxes. On the KITTI dataset, experimental evaluation is performed and the analysis results demonstrate that the proposed detection model can effectively improve the multiple target detection accuracy, and the mean average accuracy (mAP) of the improved YOLOv4 model reaches 81.23%, which is 3.18% higher than the original YOLOv4; and the computation speed of the proposed model reaches 47.32 FPS. Compared with existing popular detection models, the proposed model produces higher detection accuracy and computation speed.

Section: Introductionmentioning

confidence: 99%

A New Deep Model for Detecting Multiple Moving Targets in Real Traffic Scenarios: Machine Vision-Based Vehicles

Xiong

Liu

et al. 2022

“…Benenson et al [6], Nguyen et al [7], Antonio and Romero [8], Ragesh and Rajesh [9], Gilroy et al [10] performed a review on pedestrian detection algorithms. At the time, Benenson et al [6] had reviewed the most relevant algorithms for the 10 previous years.…”

Section: Introductionmentioning

confidence: 99%

“…They also only reported algorithms that were trained and tested on Caltech and CityScape datasets. Gilroy et al [10] is one of the most recent pedestrian detection review papers, but their review is only focused on the algorithms used to handle occluded objects.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Galvao

Abbod

Kalganova

et al. 2021

Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.

“…Another approach for addressing the issues of pedestrian recognition in blind areas is a remote sensing-based scheme using cameras and lidars [ 9 ]. Because these sensors can recognize different measurement areas, the sensor fusion approach to detect pedestrians in the blind area (occluded pedestrians) has been studied [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Detection in Blind Area and Motion Classification Based on Rush-Out Risk Using Micro-Doppler Radar

Hayashi

Saho

Daiki

et al. 2021

Various remote sensing technologies have been applied in intelligent vehicles and robots for surrounding-environment recognition. However, these technologies experience difficulties in detecting pedestrians in blind areas and their motions, such as rush-out behaviors. To address this issue, we present a radar-based technique for the detection of pedestrians in blind areas and the classification of different risks of rush-out behaviors among detected pedestrians. We verify their ability to detect pedestrian motion in blind areas by conducting experiments in two environments with blind areas formed by outdoor cars and indoor walls. Then, the classification of motions with different risks of rush-out behaviors among pedestrians detected in the blind areas is demonstrated. We use the clustering method to accurately classify several types of behaviors with different rush-out risks in both environments.