Accuracy Of Pedestrian Detection Research Articles

Progressive supervised pedestrian detection algorithm for green edge–cloud computing

In this paper, we present a novel supervised pedestrian detection algorithm tailored for Green Edge–Cloud Computing (GECC), aimed at addressing the challenges associated with pre-trained models in real-world settings. These models often exhibit performance degradation due to the divergence between training datasets and complex real-world scenarios. To bridge this gap, our proposed algorithm employs a progressive occlusion-aware iterative training strategy that significantly enhances the representation of occluded pedestrians, a prevailing issue in dense urban environments. Additionally, we introduce a K-stratified hard sampling strategy, which accelerates the training process and substantially lowers the energy consumption required for model retraining. We also explore a refined non-maximal value suppression algorithm that employs neighboring centroids separation, improving the precision of pedestrian detection within crowded scenes. Through rigorous experiments on established pedestrian detection datasets, we demonstrate that our algorithm not only elevates pedestrian detection accuracy in high-density situations but also markedly reduces the model training duration.

Cross-modality feature fusion for night pedestrian detection

Night pedestrian detection with visible image only suffers from the dilemma of high miss rate due to poor illumination conditions. Cross-modality fusion can ameliorate this dilemma by providing complementary information to each other through infrared and visible images. In this paper, we propose a cross-modal fusion framework based on YOLOv5, which is aimed at addressing the challenges of night pedestrian detection under low-light conditions. The framework employs a dual-stream architecture that processes visible images and infrared images separately. Through the Cross-Modal Feature Rectification Module (CMFRM), visible and infrared features are finely tuned on a granular level, leveraging their spatial correlations to focus on complementary information and substantially reduce uncertainty and noise from different modalities. Additionally, we have introduced a two-stage Feature Fusion Module (FFM), with the first stage introducing a cross-attention mechanism for cross-modal global reasoning, and the second stage using a mixed channel embedding to produce enhanced feature outputs. Moreover, our method involves multi-dimensional interaction, not only correcting feature maps in terms of channel and spatial dimensions but also applying cross-attention at the sequence processing level, which is critical for the effective generalization of cross-modal feature combinations. In summary, our research significantly enhances the accuracy and robustness of nighttime pedestrian detection, offering new perspectives and technical pathways for visual information processing in low-light environments.

Aero-YOLO: An Efficient Vehicle and Pedestrian Detection Algorithm Based on Unmanned Aerial Imagery

The cost-effectiveness, compact size, and inherent flexibility of UAV technology have garnered significant attention. Utilizing sensors, UAVs capture ground-based targets, offering a novel perspective for aerial target detection and data collection. However, traditional UAV aerial image recognition techniques suffer from various drawbacks, including limited payload capacity, resulting in insufficient computing power, low recognition accuracy due to small target sizes in images, and missed detections caused by dense target arrangements. To address these challenges, this study proposes a lightweight UAV image target detection method based on YOLOv8, named Aero-YOLO. The specific approach involves replacing the original Conv module with GSConv and substituting the C2f module with C3 to reduce model parameters, extend the receptive field, and enhance computational efficiency. Furthermore, the introduction of the CoordAtt and shuffle attention mechanisms enhances feature extraction, which is particularly beneficial for detecting small vehicles from a UAV perspective. Lastly, three new parameter specifications for YOLOv8 are proposed to meet the requirements of different application scenarios. Experimental evaluations were conducted on the UAV-ROD and VisDrone2019 datasets. The results demonstrate that the algorithm proposed in this study improves the accuracy and speed of vehicle and pedestrian detection, exhibiting robust performance across various angles, heights, and imaging conditions.

WS-SSD: Achieving faster 3D object detection for autonomous driving via weighted point cloud sampling

Due to the limited computational resources of the onboard computing devices of autonomous vehicles, the development of lightweight 3D object detectors is essential. Point-based detectors that progressively sample raw point clouds reduce numerous redundant computations and facilitate the implementation of high-speed 3D object detectors. The farthest point sampling based on Euclidean distance (D-FPS) is frequently utilized in point-based 3D detectors for point cloud sampling to reduce computation overhead. D-FPS can ensure uniform point sampling, covering the entire point cloud space as much as possible. The ratio of foreground and background points does not change significantly in the sampling results; hence, foreground objects do not receive sufficient attention. In addition, the number of road reflection points is large, and these points are close to foreground objects, reducing the sampling accuracy for faint objects. We propose weighted farthest point sampling based on Euclidean distance (W-DFPS), which selectively discards some road reflection points in the point sampling process, thus increasing the weight of foreground points in sampling results. It reduces the likelihood of faint objects being lost in the sampling results, such that a small number of sampling points can also cover most foreground objects. W-DFPS replaces D-FPS in the single-stage detector based on instance-aware (IA-SSD), and the network structure is slightly modified, namely the single-stage detector based on weighted sampling (WS-SSD). We evaluate WS-SSD in the KITTI dataset with a single A100 GPU. When the number of sampling points for the first module of WS-SSD is consistent with IA-SSD, the pedestrian detection accuracy is improved by an average of 4.06 compared to IA-SSD. Although the number of sampling points for the first module of WS-SSD is reduced to 25% of IA-SSD, the object detection accuracy remains competitive; it also achieves a state-of-the-art inference speed of 150.76 frames per second (FPS), which is a 46% improvement over IA-SSD.

PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8

In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feature extraction for small object detection. Secondly, to address the challenge of missed detections, we opt to integrate the CBAM into the neck of the YOLOv8. Both the channel and the spatial attention modules are used in this addition because of how well they extract feature information flow across the network. Finally, we employ Soft-NMS to improve the accuracy of pedestrian and vehicle detection in occlusion situations. Soft-NMS increases performance and manages overlapped boundary boxes well. The proposed network reduced the fraction of small objects overlooked and enhanced model detection performance. Performance comparisons with different YOLO versions ( for example YOLOv3 extremely small, YOLOv5, YOLOv6, and YOLOv7), YOLOv8 variants (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), and classical object detectors (Faster-RCNN, Cascade R-CNN, RetinaNet, and CenterNet) were used to validate the superiority of the proposed PVswin-YOLOv8s model. The efficiency of the PVswin-YOLOv8s model was confirmed by the experimental findings, which showed a 4.8% increase in average detection accuracy (mAP) compared to YOLOv8s on the VisDrone2019 dataset.

PED-AI: Pedestrian Detection for Autonomous Vehicles using YOLOv5

Pedestrian detection is crucial for autonomous vehicles, surveillance, and pedestrian safety. This abstract introduces a novel pedestrian detection method using the YOLOv5 algorithm, known for its real-time object detection prowess. The approach aims to enhance pedestrian detection accuracy across diverse lighting conditions. Methodologically, the process involves data preparation, YOLOv5 model training, and subsequent evaluation. The architecture of YOLOv5, which employs anchor boxes and a single-pass convolutional neural network, allows for quick and accurate pedestrian identification. YOLOv5's design, which includes anchor boxes and a single-pass convolutional neural network, enables speedy and accurate pedestrian recognition. Study tests confirm the efficacy of the YOLOv5-based approach. In the first scenario, the model detected pedestrians in daylight with 75% accuracy, but it also produced 11 false negatives or a 25% miss. Although Scenario 2's accuracy was higher at 85%, there were still 11 false negatives, which suggested that there was a persistent detection gap. In spite of these outcomes, the YOLOv5 model demonstrates the possibility of accurate pedestrian detection in real-world settings. While it greatly improves applications like self-driving cars and pedestrian safety, lowering false negatives remains a primary goal for increasing overall accuracy. The investigation's findings show that YOLOv5 can function in a variety of lighting conditions, but also highlight the necessity for further work in order to meet stringent detection requirements.

Research Advanced in Pedestrian Detection Based on Deep Learning

In the field of computer vision, pedestrian detection is a significant research area that is frequently applied in areas like intelligent driving and video surveillance. The reliability and accuracy of subsequent tasks like pedestrian tracking and behavior analysis are directly impacted by the accuracy and effectiveness of pedestrian detection. However, there are still a number of difficulties with pedestrian detection tasks because of the variety of factors, including the shape, scale, and posture of pedestrians, as well as background interference and changes in lighting. Based on research on pertinent domestic and international literature, this article examines the key techniques for identifying pedestrians. Introduce frequently used datasets for pedestrian detection first. The model can then be split into two categories based on its training process: one-stage detection algorithms and two-stage detection algorithms. For each stage type, this study offers a thorough introduction to representative algorithms. A summary of its current state of development and future prospects was then given.

Pedestrian detection algorithm integrating large kernel attention and YOLOV5 lightweight model.

In the context of intelligent driving, pedestrian detection faces challenges related to low accuracy in target recognition and positioning. To address this issue, a pedestrian detection algorithm is proposed that integrates a large kernel attention mechanism with the YOLOV5 lightweight model. The algorithm aims to enhance long-term attention and dependence during image processing by fusing the large kernel attention module with the C3 module. Furthermore, it addresses the lack of long-distance relationship information in channel and spatial feature extraction and representation by introducing the Coordinate Attention mechanism. This mechanism effectively extracts local information and focused location details, thereby improving detection accuracy. To improve the positioning accuracy of obscured targets, the alpha CIOU bounding box regression loss function is employed. It helps mitigate the impact of occlusions and enhances the algorithm's ability to precisely localize pedestrians. To evaluate the effectiveness of trained model, experiments are conducted on the BDD100K pedestrian dataset as well as the Pascal VOC dataset. Experimental results demonstrate that the improved attention fusion YOLOV5 lightweight model achieves an average accuracy of 60.3%. Specifically, the detection accuracy improves by 1.1% compared to the original YOLOV5 algorithm, and the accuracy performance index reaches 73.0%. These findings strongly indicate the proposed algorithm in significantly enhancing the accuracy of pedestrian detection in road scenes.

Improved YOLOv3 Integrating SENet and Optimized GIoU Loss for Occluded Pedestrian Detection.

Occluded pedestrian detection faces huge challenges. False positives and false negatives in crowd occlusion scenes will reduce the accuracy of occluded pedestrian detection. To overcome this problem, we proposed an improved you-only-look-once version 3 (YOLOv3) based on squeeze-and-excitation networks (SENet) and optimized generalized intersection over union (GIoU) loss for occluded pedestrian detection, namely YOLOv3-Occlusion (YOLOv3-Occ). The proposed network model considered incorporating squeeze-and-excitation networks (SENet) into YOLOv3, which assigned greater weights to the features of unobstructed parts of pedestrians to solve the problem of feature extraction against unsheltered parts. For the loss function, a new generalized intersection over unionintersection over groundtruth (GIoUIoG) loss was developed to ensure the areas of predicted frames of pedestrian invariant based on the GIoU loss, which tackled the problem of inaccurate positioning of pedestrians. The proposed method, YOLOv3-Occ, was validated on the CityPersons and COCO2014 datasets. Experimental results show the proposed method could obtain 1.2% MR-2 gains on the CityPersons dataset and 0.7% mAP@50 improvements on the COCO2014 dataset.

Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian

Crowding and occlusion pose significant challenges for pedestrian detection, which can easily lead to missed and false detections for small-scale and occluded pedestrian objects in dense pedestrian scenarios. To enhance dense pedestrian detection accuracy, we propose the Residual Transformer YOLO (RT-YOLO) algorithm in this paper. The RT-YOLO algorithm enhances the multi-scale fusion strategy based on YOLOv7 and introduces a dedicated detection layer for small-scale occluded targets. It also integrates Resnet and Transformer structures to improve the small-scale feature layer and detection head, enhancing feature extraction capabilities. Additionally, the RT-YOLO algorithm incorporates the Normalization-based Attention Module (NAM) into the backbone and neck networks to identify the region of interest. The experiments demonstrate that on the CrowdHuman and WiderPerson datasets, at IOU (Intersection over Union) = 0.5, the overall improvement in mAP50 is 3.8% and 3.4%. In the IOU range from 0.5 to 1, the improvement in mAP50: 95 is 5.1% and 4%. RT-YOLO achieves an FPS of 67, maintaining real-time performance. On the VOC2007 dataset, mAP50 has been enhanced by 5.1%, indicating higher effectiveness and robustness.

Estimating pedestrian traffic with Bluetooth sensor technology

ABSTRACT The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research. Bluetooth technology, for example, is already vastly used in vehicular transportation management solutions and services. Current studies discuss the potential of this emerging technology for pedestrian mobility research, but it has yet to be examined in a large urban setting. One of the main problems is detecting pedestrians from Bluetooth records since their behavior and movement patterns share similarities with other urban transportation modes. This study aims to accurately detect pedestrians using a network of 65 Bluetooth detectors located in Tel-Aviv, Israel, which record on average over 60,000 unique daily Bluetooth Media-Access-Control addresses. We propose a detection methodology that includes system calibration, effective travel time calculation, and classification by velocity that takes into consideration the probability of vehicular traffic jams. An evaluation of the proposed methodology presents a promising pedestrian detection accuracy rate of 89%. We showcase the results of pedestrian traffic analysis, together with a discussion on the data analysis challenges and limitations. To the best of our knowledge, this work is the first to analyze pedestrian records detection from a Bluetooth network employed in a dynamic urban environment setting.

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection.

Infrared pedestrian target detection is affected by factors such as the low resolution and contrast of infrared pedestrian images, as well as the complexity of the background and the presence of multiple targets occluding each other, resulting in indistinct target features. To address these issues, this paper proposes a method to enhance the accuracy of pedestrian target detection by employing contour information to guide multi-scale feature detection. This involves analyzing the shapes and edges of the targets in infrared images at different scales to more accurately identify and differentiate them from the background and other targets. First, we propose a preprocessing method to suppress background interference and extract color information from visible images. Second, we propose an information fusion residual block combining a U-shaped structure and residual connection to form a feature extraction network. Then, we propose an attention mechanism based on a contour information-guided approach to guide the network to extract the depth features of pedestrian targets. Finally, we use the clustering method of mIoU to generate anchor frame sizes applicable to the KAIST pedestrian dataset and propose a hybrid loss function to enhance the network's adaptability to pedestrian targets. The extensive experimental results show that the method proposed in this paper outperforms other comparative algorithms in pedestrian detection, proving its superiority.

A Social Distance Monitoring Method Based on Improved YOLOv4 for Surveillance Videos

Social distance monitoring is of great significance for public health in the era of COVID-19 pandemic. However, existing monitoring methods cannot effectively detect social distance in terms of efficiency, accuracy, and robustness. In this paper, we proposed a social distance monitoring method based on an improved YOLOv4 algorithm. Specifically, our method constructs and pre-processes a dataset. Afterwards, our method screens the valid samples and improves the K-means clustering algorithm based on the IoU distance. Then, our method detects the target pedestrians using a trained improved YOLOv4 algorithm and gets the pedestrian target detection frame location information. Finally, our method defines the observation depth parameters, generates the 3D feature space, and clusters the offending aggregation groups based on the L2 parametric distance to finally realize the pedestrian social distance monitoring of 2D video. Experiments show that the proposed social distance monitoring method based on improved YOLOv4 can accurately detect pedestrian target locations in video images, where the pre-processing operation and improved K-means algorithm can improve the pedestrian target detection accuracy. Our method can cluster the offending groups without going through calibration mapping transformation to realize the pedestrian social distance monitoring of 2D videos.

A Small Target Pedestrian Detection Model Based on Autonomous Driving

Since small-target pedestrians account for a small proportion of pixels in images and lack texture features, the feature information of small-target pedestrians is often ignored in the feature extraction process, leading to reduced accuracy and poor robustness. To improve the accuracy of small-target pedestrian detection and the anti-interference ability of the model, a small-target pedestrian detection model that fuses residual networks and feature pyramids is proposed. First, a residual block with a discard layer is constructed to replace the standard residual block in the residual network structure to reduce the complexity of the model computation process and solve the problems of gradient disappearance and explosion in the deep network. Then, feature selection and feature alignment modules are added to the lateral connection part of the feature pyramid to enhance important pedestrian features in the input image, and the multiscale feature fusion capability of the model is enhanced for small-target pedestrians, thereby improving the detection accuracy of small-target pedestrians and solving the problems of feature misalignment and ignored multiscale features in the feature pyramid network. Finally, a cascaded autofocus query module is proposed to increase the inference speed of the feature pyramid network through focusing and querying, thus improving the performance and efficiency of small-target pedestrian detection. The experimental results show that the proposed model achieves better detection results than previous models.

IVP-YOLOv5: an intelligent vehicle-pedestrian detection method based on YOLOv5s

Computer vision is now vital in intelligent vehicle environment perception systems. However, real-time small-scale pedestrian detection in intelligent vehicle environment perception systems is still needs to be improved. This paper proposes an intelligent vehicle-pedestrian detection method based on YOLOv5s, named IVP-YOLOv5, to use in vehicle environment perception systems. Based on the network structure of YOLOv5s, we replaced BottleNeck CSP with Ghost-Bottleneck to reduce the complexity of processing feature maps while maintaining good detection performance. To reduce the error between the ground truth box and the predicted box, we apply Alpha-IoU as the bounding box loss function, improving pedestrian detection accuracy and robustness. We introduce the slicing-aided hyper inference (SAHI) strategy, which enables the lightweight backbone network to capture more detailed features of pedestrians by enlarging image pixels. Experiments on the BDD100 K dataset show that the proposed IVP-YOLOv5 achieves 67.1% AP and 18.5% APs of pedestrian detection, and the GFLOPs and the number of parameters are only 10.5 and 4.9M.

A Smart Metro Passenger Detector Based on Two Mode MetroNexts

Accurate metro passenger detection is considered a fundamental technique for the smart metro station. The major challenge is false positives (FPs) when hunting for passengers in metro carriages and stations. This article provides an in-depth analysis to reveal the reasons for this problem in the two-stage convolutional neural network (CNN)-based detector and proposes a novel two-mode refined proposals algorithm to address the problem, which includes cascade mode and intrinsic information mode, or CRP and IRP algorithms for short. Aided by the two-mode refined Proposals algorithm, this article designs two small but fast and accurate pedestrian detectors: MetroNext-CRP and MetroNext-IRP to meet the application requirements of different tasks. Based on various challenging benchmark datasets and two metro scene datasets, the experimental results have demonstrated that the two-mode refined proposals algorithm is effective and can improve pedestrian detection accuracy by removing the FPs. Compared with the existing state-of-the-art detection networks, MetroNext-CRP achieves competitive detection results with acceptable computational cost and MetroNext-IRP demonstrates better detection accuracy with fast inference speed and without extra memory consumption, thus providing a practical solution for pedestrian detection tasks on various hardware platforms, particularly tailored to edge devices. Finally, the two-mode MetroNexts are deployed in a metro’s onboard computer, and the experiments in real-life campus and metro scenes further demonstrate the feasibility of the two detectors, especially the field experiments in metro scenes strongly support that they can be served as a smart instrument to provide accurate passenger position information to metro operators in complicated and realistic metro scenes.

A novel small-scale pedestrian detection method base on residual block group of CenterNet

Pedestrian detection is widely used in intelligent supervision and assisted driving. With the development of deep learning, the accuracy of pedestrian detection has been greatly improved. In actual scenes, there are often pedestrians who are far away from the camera. Such pedestrians usually have small image sizes, while existing algorithms still have defects such as missed detection for similar small-scale pedestrian detection, which will reduce the accuracy of operation. Therefore, this paper designs a Three ResNet Blocks based on CenterNet detection model. Aiming at the limited ability of a single feature extraction block to extract semantic information at different levels in the network, this paper proposes Three ResNet Blocks, which is a simple and effective multi-block group. This block group integrates three different basic blocks, each of which extracts pedestrian information separately to enhance information flow in the network structure and make detection results more accurate. In addition, combined with the advantages of activation function in the model expression, the relu6 activation function is introduced to improve the performance of the detector by preventing numerical explosion being sensitive to decimal. Comprehensive experiments on pedestrian detection datasets (Caltech and ETH) show that the proposed method exhibits excellent accuracy and detection speed, especially for small-scale pedestrian detection.

Pedestrian detection based on YOLOv3 multimodal data fusion

Multi-sensor fusion has essential applications in the field of target detection. Considering the current actual demand for miniaturization of on-board computers for driverless vehicles, this paper uses the multimodal data YOLOv3 (MDY) algorithm for pedestrian detection on embedded devices. The MDY algorithm uses YOLOv3 as the basic framework to improve pedestrian detection accuracy by optimizing anchor frames and adding small target detection branches. Then the algorithm is accelerated by using TensorRT technology to improve the real-time performance in embedded devices. Finally, a hybrid fusion framework is used to fuse the LIDAR point cloud data with the improved YOLOv3 algorithm to compensate for the shortcomings of a single sensor and improve the detection accuracy while ensuring speed. The improved YOLOv3 improves AP by 6.4% and speed by 11.3 FPS over the original algorithm. The MDY algorithm achieves better performance on the KITTI dataset. To further verify the feasibility of the MDY algorithm, an actual test was conducted on an unmanned vehicle with Jetson TX2 embedded device as the on-board computer within the campus scenario, and the results showed that the MDY algorithm achieves 90.8% accuracy under real-time operation and can achieve adequate detection accuracy and real-time performance on the embedded device.

Towards High Accuracy Pedestrian Detection on Edge GPUs.

Despite the rapid development of pedestrian detection algorithms, the balance between detection accuracy and efficiency is still far from being achieved due to edge GPUs (low computing power) limiting the parameters of the model. To address this issue, we propose the YOLOv4-TP-Tiny based on the YOLOv4 model, which mainly includes two modules, two-dimensional attention (TA) and pedestrian-based feature extraction (PFM). First, we integrate the TA mechanism into the backbone network, which increases the attention of the network to the visible area of pedestrians and improves the accuracy of pedestrian detection. Then, the PFM is used to replace the original spatial pyramid pooling (SPP) structure in the YOLOv4 to obtain the YOLOv4-TP algorithm, which can adapt to different sizes of people to obtain higher detection accuracy. To maintain detection speed, we replaced the normal convolution with a ghost network with a TA mechanism, resulting in more feature maps with fewer parameters. We constructed a one-way multi-scale feature fusion structure to replace the down-sampling process, thereby reducing network parameters to obtain the YOLOv4-TP-Tiny model. The experimental results show that the YOLOv4-TP-tiny has 58.3% AP and 31 FPS in the winder person pedestrian dataset. With the same hardware conditions and dataset, the AP of the YOLOv4-tiny is 55.9%, and the FPS is 29.

YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection.

In response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate of target pedestrians, and the poor retention of non-redundant boxes, YOLOv5 is adopted as the baseline to improve the effectiveness of pedestrian detection. First of all, L1 regularization is deployed before the BN layer, and the layers with smaller influence factors are removed through sparse training to achieve the effect of model pruning. In the next moment, the context extraction module is applied to the feature extraction network, and the input features are fully extracted using receptive fields of different sizes. In addition, both the context attention module CxAM and the content attention module CnAM are added to the FPN part to correct the target position deviation in the process of feature extraction so that the accuracy of detection can be improved. What is more, DIoU_NMS is employed to replace NMS as the prediction frame screening algorithm to improve the problem of detection target loss in the case of high target coincidence. Experimental results show that compared with YOLOv5, the AP of our YOLOv5-AC model for pedestrians is 95.14%, the recall is 94.22%, and the counting frame rate is 63.1 FPS. Among them, AP and recall increased by 3.78% and 3.92%, respectively, while the detection speed increased by 57.8%. The experimental results verify that our YOLOv5-AC is an effective and accurate method for pedestrian detection in railways.