BwimNet: A Novel Method for Identifying Moving Vehicles Utilizing a Modified Encoder-Decoder Architecture

Wu, Yuanzhi; Deng, Lu; He, Wang

doi:10.3390/s20247170

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…Moreover, the ALPD module, the indirect detection branch, and the whole detection network have almost the same vehicle detection performance as the vanilla SSD [36]. This way, it proves our method can continuously improve the license plate detection performance while maintaining the vehicle detection performance [43][44][45][46][47].…”

Section: Ablation Studymentioning

confidence: 55%

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

Chen

Liu

et al. 2021

Sensors

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As the license plate is multiscale and multidirectional in the natural scene image, its detection is challenging in many applications. In this work, a novel network that combines indirect and direct branches is proposed for license plate detection in the wild. The indirect detection branch performs small-sized vehicle plate detection with high precision in a coarse-to-fine scheme using vehicle–plate relationships. The direct detection branch detects the license plate directly in the input image, reducing false negatives in the indirect detection branch due to the miss of vehicles’ detection. We propose a universal multidirectional license plate refinement method by localizing the four corners of the license plate. Finally, we construct an end-to-end trainable network for license plate detection by combining these two branches via post-processing operations. The network can effectively detect the small-sized license plate and localize the multidirectional license plate in real applications. To our knowledge, the proposed method is the first one that combines indirect and direct methods into an end-to-end network for license plate detection. Extensive experiments verify that our method outperforms the indirect methods and direct methods significantly.

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Section: Ablation Studymentioning

confidence: 55%

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

Chen

Liu

et al. 2021

Sensors

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“…Existing methods for predicting vessel trajectory fall into three categories: traditional [13,14], machine learning [15,16], and deep learning [17][18][19]. Traditional approaches primarily rely on empirical and mathematical models following specific physical laws [20,21].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

Xiao,

Hu,

Liu

et al. 2024

JMSE

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Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical spatio-temporal Automatic Identification System (AIS) data. However, environmental factors such as sea wind and visibility also affect ship navigation in real-world maritime shipping. Therefore, developing reliable models utilizing multimodal data, such as AIS and environmental data, is challenging. In this research, we design an adaptive multimodal vessel trajectory data prediction model (termed AMD) based on satellite AIS and environmental data. The AMD model mainly consists of an AIS-based extraction network, an environmental-based extraction network, and a fusion block. In particular, this work considers multimodal data such as historical spatio-temporal information and environmental factors. Time stamps and distances are correlated with AIS and environmental data, and a multilayer perceptron and gated recurrent unit networks are used to design multimodal feature extraction networks. Finally, the fusion block realizes the fusion output of multimodal features to improve the reliability of the AMD model. Several quantitative and qualitative experiments are conducted using real-world AIS and multimodal environmental datasets. Numerous experimental results prove that prediction performance using multimodal data can ensure satisfactory accuracy and reliability while exhibiting a positive impact on improving maritime transport services.

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“…Its high precision and broad applicability has attracted many scholars. Wu et al [ 11 ] developed an encoder–decoder structure called BwimNet to identify the properties of moving vehicles. The model is multi-target and can detect axle number, speed, weight, and wheelbases simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Identification and Tracking of Vehicles between Multiple Cameras on Bridges Using a YOLOv4 and OSNet-Based Method

Jin

et al. 2023

Sensors

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The estimation of vehicle loads is a rising research hotspot in bridge structure health monitoring (SHM). Traditional methods, such as the bridge weight-in-motion system (BWIM), are widely used but they fail to record the locations of vehicles on the bridges. Computer vision-based approaches are promising ways for vehicle tracking on bridges. Nevertheless, keeping track of vehicles from the video frames of multiple cameras without an overlapped visual field poses a challenge for the tracking of vehicles across the whole bridge. In this study, a method that was You Only Look Once v4 (YOLOv4)- and Omni-Scale Net (OSNet)-based was proposed to realize vehicle detecting and tracking across multiple cameras. A modified IoU-based tracking method was proposed to track a vehicle in adjacent video frames from the same camera, which takes both the appearance of vehicles and overlapping rates between the vehicle bounding boxes into consideration. The Hungary algorithm was adopted to match vehicle photos in various videos. Moreover, a dataset with 25,080 images of 1727 vehicles for vehicle identification was established to train and evaluate four models. Field validation experiments based on videos from three surveillance cameras were conducted to validate the proposed method. Experimental results show that the proposed method has an accuracy of 97.7% in terms of vehicle tracking in the visual field of a single camera and over 92.5% in tracking across multiple cameras, which can contribute to the acquisition of the temporal–spatial distribution of vehicle loads on the whole bridge.

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BwimNet: A Novel Method for Identifying Moving Vehicles Utilizing a Modified Encoder-Decoder Architecture

Cited by 8 publications

References 44 publications

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches

An Adaptive Multimodal Data Vessel Trajectory Prediction Model Based on a Satellite Automatic Identification System and Environmental Data

Identification and Tracking of Vehicles between Multiple Cameras on Bridges Using a YOLOv4 and OSNet-Based Method

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