In response to challenges such as the large number of parameters and high computational demands of vehicle appearance damage detection models, which hinder deployment on mobile devices, this paper presents a study focusing on lightweight and high-precision optimization of the YOLOv5s target detection algorithm. Specifically, we introduce the lightweight network into the YOLOv5s architecture to create a more efficient network. Furthermore, we integrate the attention mechanism to enhance feature extraction capabilities and employ knowledge distillation to improve algorithm accuracy. These enhancements aim to boost target detection performance. The experimental results illustrate that our optimized YOLOv5 algorithm achieves significant improvements in both speed and accuracy on the car damage dataset.
KEYWORDS: Video, Denoising, RGB color model, Data modeling, Principal component analysis, Video processing, Visualization, Optimization (mathematics), Image processing, Fourier transforms
Video denoising is an elementary but critical task in computer vision and has been widely studied in recent years. However, the existing denoising methods have inevitable drawbacks: some need to predefine rank, some ignore the local information, and most cannot deal with higher-order data. To overcome these shortcomings, we consider two high-order tensor low-rank approximation methods, aiming to achieve color video denoising in a mixed noise environment. First, we establish a high-order tensor framework. Based on this framework, high-order tensor robust principal component analysis (HRPCA) is proposed. Although HRPCA is capable of processing high-order data, there is still a loss of recovery details. Then, we develop another method called high-order tensor low-rank approximation with total variation regularization (HTV). In particular, the TV consists of frontal total variation (FTV) and global total variation (GTV), thus extending the HTV into HFTV and HGTV, respectively. Extensive experimental results of color videos show that the HRPCA and HTV are more efficient in dealing with denoising problems than other state-of-the-art methods.
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