Light-weighted vehicle detection network based on improved YOLOv3-tiny

Ge, Pingshu; Guo, Lie; He, Danni; Huang, Liang

doi:10.1177/15501329221080665

Cited by 13 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the literature, 34 a large p leads to a dense weight distribution, and the sparsity of the neural network is significantly negatively correlated with p. In other words, the sparsity of the neural network is determined by parameter p. To prune the redundant weights with absolute values less than the threshold, we construct the corresponding augmented empirical risk minimization as follows:…”

Section: Sparse Training Methods For the Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Zhang,

2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Vehicle detection using aerial thermal infrared images has received significant attention because of its strong capability for day and night observations to supply information for vehicle tracking, traffic monitoring, and road network planning. Compared with aerial visible images, aerial thermal infrared images are not sensitive to lighting conditions. However, they have low contrast and blurred edges. Therefore, a combinational and sparse you-only-look-once (ComS-YOLO) neural network is put forward to accurately and quickly detect vehicles in aerial thermal infrared images. Therein, we adjust the structure of the deep neural network to balance the detection accuracy and running time. In addition, we propose an objective function that utilizes the diagonal distance of the corresponding minimum external rectangle, which prevents non-convergence when there is an inclusion relationship between the prediction and true boxes or in the case of width and height alignment. Furthermore, to avoid over-fitting in the training stage, we eliminate some redundant parameters via constraints and on-line pruning. Finally, experimental results on the NWPU VHR-10 and DARPA VIVID datasets show that the proposed ComS-YOLO network effectively and efficiently identifies the vehicles with a low missed rate and false detection rate. Compared with the Faster R-CNN and a series of YOLO neural networks, the proposed neural network presents satisfactory and competitive results in terms of the detection accuracy and running time. Furthermore, vehicle detection experiments under different environments are also carried out, which shows that our method can achieve an excellent and desired performance on detection accuracy and robustness of vehicle detection.

show abstract

Section: Sparse Training Methods For the Neural Networkmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

Zhang,

2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

“…Considering the requirement of lightweight models in edge computing platforms, we choose YOLOv4-Tiny released on 25 June 2020 [34]. Compared with YOLOv3-Tiny [36], it is a huge improvement. Although YOLOv4-tiny is able to recognize the input images in real time, the accuracy is still not high enough, because the installation position of the cameras in public space causes different people and objects to be at different distances from the cameras, so the size and location of the detected targets are different.…”

Section: Improved Target Detection Modelmentioning

confidence: 99%

A Multidimensional Data Collection and Edge Computing Analysis Method

Ji,

Li,

Zhao

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

With the development of IoT technology, many dimensions of data are generated in the environment where we live. The study of these data is critical to our understanding of the relationships between people and between people and cities. The core components of IoT technology are sensors and control circuits. However, merging various sensor data and real-time data processing is often a difficult problem, usually related to factors such as coverage, lighting conditions, and accuracy of object detection. Therefore, we firstly propose a wireless transmission hardware architecture for data acquisition mainly based on vision sensors, and at the same time, incorporate some sensors for data calibration to improve the accuracy of data detection. The collected data are fed back to the edge computing platform for fast processing. The edge platform is designed with a lightweight target detection model and data analysis model. Through this multidimensional data collection and analysis, a generalised functional model for public space utilization can be fitted, which enables the calculation of utilization rates for any parameter in public space. The technology improves a technical reference for multi-dimensional data collection and analysis.

show abstract

“…According to the relevant datasets, the accuracy of the proposed model increased by 3.2%, the number of flops decreased by 15.24%, and the number of model parameters decreased by 19.37%. Ge [ 22 , 23 , 24 ] improved the Yoyov3 model by using lightweight networks such as the darknet-19 and ResNet-18 networks to realize feature extraction and training and testing the Kitti datasets, and compared with the traditional Yoyov3 dataset, the average accuracy increased by 14.09%, reaching 93.66%.…”

Section: Introductionmentioning

confidence: 99%

DV3-IBi_YOLOv5s: A Lightweight Backbone Network and Multiscale Neck Network Vehicle Detection Algorithm

Wang,

Shi,

Zhao

et al. 2024

Sensors

View full text Add to dashboard Cite

Vehicle detection is a research direction in the field of target detection and is widely used in intelligent transportation, automatic driving, urban planning, and other fields. To balance the high-speed advantage of lightweight networks and the high-precision advantage of multiscale networks, a vehicle detection algorithm based on a lightweight backbone network and a multiscale neck network is proposed. The mobile NetV3 lightweight network based on deep separable convolution is used as the backbone network to improve the speed of vehicle detection. The icbam attention mechanism module is used to strengthen the processing of the vehicle feature information detected by the backbone network to enrich the input information of the neck network. The bifpn and icbam attention mechanism modules are integrated into the neck network to improve the detection accuracy of vehicles of different sizes and categories. A vehicle detection experiment on the Ua-Detrac dataset verifies that the proposed algorithm can effectively balance vehicle detection accuracy and speed. The detection accuracy is 71.19%, the number of parameters is 3.8 MB, and the detection speed is 120.02 fps, which meets the actual requirements of the parameter quantity, detection speed, and accuracy of the vehicle detection algorithm embedded in the mobile device.

show abstract

Light-weighted vehicle detection network based on improved YOLOv3-tiny

Cited by 13 publications

References 27 publications

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

ComS-YOLO: a combinational and sparse network for detecting vehicles in aerial thermal infrared images

A Multidimensional Data Collection and Edge Computing Analysis Method

DV3-IBi_YOLOv5s: A Lightweight Backbone Network and Multiscale Neck Network Vehicle Detection Algorithm

Contact Info

Product

Resources

About