Integrated Compressed Sensing and YOLOv4 for Application in Image-storage and Object-recognition of Dashboard Camera

Wu, Jim-Wei; Wu, Cheng‐Chia; Cen, Wen-Shan; Chao, Shao-An; Weng, Jui-Tse

doi:10.1109/anzcc53563.2021.9628221

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The model achieved a 65.2% mean intersection-over-union accuracy at a detection speed of 34.4 FPS on the CamVid dataset. Wu et al [59] focused on improving the dashcam storage space and object recognition rate. The experiments revealed that the compressed sensing method of the iterative shrinkage thresholding algorithm with the network was able to reduce the storage space by 60% while maintaining image resolution.…”

Section: Studies Related To the Detection Of Objects Other Than Traff...mentioning

confidence: 99%

Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

Chung

2023

Future Internet

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Against the backdrop of rising road traffic accident rates, measures to prevent road traffic accidents have always been a pressing issue in Taiwan. Road traffic accidents are mostly caused by speeding and roadway obstacles, especially in the form of rockfalls, potholes, and car crashes (involving damaged cars and overturned cars). To address this, it was necessary to design a real-time detection system that could detect speed limit signs, rockfalls, potholes, and car crashes, which would alert drivers to make timely decisions in the event of an emergency, thereby preventing secondary car crashes. This system would also be useful for alerting the relevant authorities, enabling a rapid response to the situation. In this study, a hierarchical deep-learning-based object detection model is proposed based on You Only Look Once v7 (YOLOv7) and mask region-based convolutional neural network (Mask R-CNN) algorithms. In the first level, YOLOv7 identifies speed limit signs and rockfalls, potholes, and car crashes. In the second level, Mask R-CNN subdivides the speed limit signs into nine categories (30, 40, 50, 60, 70, 80, 90, 100, and 110 km/h). The images used in this study consisted of screen captures of dashcam footage as well as images obtained from the Tsinghua-Tencent 100K dataset, Google Street View, and Google Images searches. During model training, we employed Gaussian noise and image rotation to simulate poor weather conditions as well as obscured, slanted, or twisted objects. Canny edge detection was used to enhance the contours of the detected objects and accentuate their features. The combined use of these image-processing techniques effectively increased the quantity and variety of images in the training set. During model testing, we evaluated the model’s performance based on its mean average precision (mAP). The experimental results showed that the mAP of our proposed model was 8.6 percentage points higher than that of the YOLOv7 model—a significant improvement in the overall accuracy of the model. In addition, we tested the model using videos showing different scenarios that had not been used in the training process, finding the model to have a rapid response time and a lower overall mean error rate. To summarize, the proposed model is a good candidate for road safety detection.

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Section: Studies Related To the Detection Of Objects Other Than Traff...mentioning

confidence: 99%

Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

Chung

2023

Future Internet

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Deep Learning based Visual Object Recognition for Manipulator Grasps

Lee

Hsu

Doan

et al. 2022

2022 18th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

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Realization of a Real-Time Image Denoising System for Dashboard Camera Applications

Hou

2022

IEEE Trans. Consumer Electron.

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Integrated Compressed Sensing and YOLOv4 for Application in Image-storage and Object-recognition of Dashboard Camera

Cited by 3 publications

References 12 publications

Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

Application of an Effective Hierarchical Deep-Learning-Based Object Detection Model Integrated with Image-Processing Techniques for Detecting Speed Limit Signs, Rockfalls, Potholes, and Car Crashes

Deep Learning based Visual Object Recognition for Manipulator Grasps

Realization of a Real-Time Image Denoising System for Dashboard Camera Applications

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