Disparity-Based Multiscale Fusion Network for Transportation Detection

Chen, Jing; Wang, Qichao; Peng, Weiming; Xu, Haitao; Li, Xiaodong; Xu, Wenqiang

doi:10.1109/tits.2022.3161977

Cited by 112 publications

(35 citation statements)

References 34 publications

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“…However, there is also a subset of methods that solely utilize image-based detection models. These models primarily perform detection on the frontal views of 2D images [16][17][18]. However, most of these methods significantly lag behind lidar-based detection approaches in terms of accurately locating the positions of 3D objects.…”

Section: D Object Detectionmentioning

confidence: 99%

Real Pseudo-Lidar Point Cloud Fusion for 3D Object Detection

Fan,

Xiao,

Cai

et al. 2023

Electronics

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Three-dimensional object detection technology is an essential component of autonomous driving systems. Existing 3D object detection techniques heavily rely on expensive lidar sensors, leading to increased costs. Recently, the emergence of Pseudo-Lidar point cloud data has addressed this cost issue. However, the current methods for generating Pseudo-Lidar point clouds are relatively crude, resulting in suboptimal detection performance. This paper proposes an improved method to generate more accurate Pseudo-Lidar point clouds. The method first enhances the stereo-matching network to improve the accuracy of Pseudo-Lidar point cloud representation. Secondly, it fuses 16-Line real lidar point cloud data to obtain more precise Real Pseudo-Lidar point cloud data. Our method achieves impressive results in the popular KITTI benchmark. Our algorithm achieves an object detection accuracy of 85.5% within a range of 30 m. Additionally, the detection accuracies for pedestrians and cyclists reach 68.6% and 61.6%, respectively.

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Section: D Object Detectionmentioning

confidence: 99%

Real Pseudo-Lidar Point Cloud Fusion for 3D Object Detection

Fan,

Xiao,

Cai

et al. 2023

Electronics

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“…DCNNs have indicated their outstanding advantages in Digital Image Processing (DIP) tasks [ 1 ], such as Autonomous Underwater Vehicle (AUV) Systems [ 2 ], cognitive and social networks [ 3 , 4 ], traffic management [ 5 ], reverse auctions [ 6 ], epidemic model detection [ 7 ], predictive control [ 8 ], recognition [ 9 ], detection [ 10 ], and image classification [ 11 , 12 ]. DCNNs are initially motivated by the cat visual cortex’s computational model differentiating signal-related and processing vision tasks [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive habitat biogeography-based optimizer for optimizing deep CNN hyperparameters in image classification

Xin,

Khishe,

Zeebaree

et al. 2024

Heliyon

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“…The central goal of accident data analysis is to identify the key factors that influence the occurrence of road traffic accidents, ultimately addressing critical road safety issues. The effectiveness of accident prevention strategies predominantly relies on the authenticity of the gathered and estimated data and the suitability of the chosen analysis methods [ 11 , 12 ]. Choosing the appropriate data analysis method is crucial for revealing the causes of accidents in specific zones or study locations and for reasonably accurately predicting the likelihood of daily accident occurrences or assessing the safety levels for different groups of road users in that area [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

Aboulola

2024

PLoS ONE

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Traffic accidents remain a leading cause of fatalities, injuries, and significant disruptions on highways. Comprehending the contributing factors to these occurrences is paramount in enhancing safety on road networks. Recent studies have demonstrated the utility of predictive modeling in gaining insights into the factors that precipitate accidents. However, there has been a dearth of focus on explaining the inner workings of complex machine learning and deep learning models and the manner in which various features influence accident prediction models. As a result, there is a risk that these models may be seen as black boxes, and their findings may not be fully trusted by stakeholders. The main objective of this study is to create predictive models using various transfer learning techniques and to provide insights into the most impactful factors using Shapley values. To predict the severity of injuries in accidents, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Residual Networks (ResNet), EfficientNetB4, InceptionV3, Extreme Inception (Xception), and MobileNet are employed. Among the models, the MobileNet showed the highest results with 98.17% accuracy. Additionally, by understanding how different features affect accident prediction models, researchers can gain a deeper understanding of the factors that contribute to accidents and develop more effective interventions to prevent them.

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Disparity-Based Multiscale Fusion Network for Transportation Detection

Cited by 112 publications

References 34 publications

Real Pseudo-Lidar Point Cloud Fusion for 3D Object Detection

Real Pseudo-Lidar Point Cloud Fusion for 3D Object Detection

Adaptive habitat biogeography-based optimizer for optimizing deep CNN hyperparameters in image classification

Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique

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