Lianghao Tan

Lianghao Tan, Shubing Liu, Jing Gao et al.

With the rapid advancement of deep learning technologies, computer vision has shown immense potential in retail automation. This paper presents a novel self-checkout system for retail based on an improved YOLOv10 network, aimed at enhancing checkout efficiency and reducing labor costs. We propose targeted optimizations to the YOLOv10 model, by incorporating the detection head structure from YOLOv8, which significantly improves product recognition accuracy. Additionally, we develop a post-processing algorithm tailored for self-checkout scenarios, to further enhance the application of system. Experimental results demonstrate that our system outperforms existing methods in both product recognition accuracy and checkout speed. This research not only provides a new technical solution for retail automation but offers valuable insights into optimizing deep learning models for real-world applications.

Hongye Zheng, Bingxing Wang, Minheng Xiao et al.

Adaptive optimizers are pivotal in guiding the weight updates of deep neural networks, yet they often face challenges such as poor generalization and oscillation issues. To counter these, we introduce sigSignGrad and tanhSignGrad, two novel optimizers that integrate adaptive friction coefficients based on the Sigmoid and Tanh functions, respectively. These algorithms leverage short-term gradient information, a feature overlooked in traditional Adam variants like diffGrad and AngularGrad, to enhance parameter updates and convergence.Our theoretical analysis demonstrates the wide-ranging adjustment capability of the friction coefficient S, which aligns with targeted parameter update strategies and outperforms existing methods in both optimization trajectory smoothness and convergence rate. Extensive experiments on CIFAR-10, CIFAR-100, and Mini-ImageNet datasets using ResNet50 and ViT architectures confirm the superior performance of our proposed optimizers, showcasing improved accuracy and reduced training time. The innovative approach of integrating adaptive friction coefficients as plug-ins into existing optimizers, exemplified by the sigSignAdamW and sigSignAdamP variants, presents a promising strategy for boosting the optimization performance of established algorithms. The findings of this study contribute to the advancement of optimizer design in deep learning.

Xiaoyi Liu, Zhou Yu, Lianghao Tan et al.

Skin lesions are an increasingly significant medical concern, varying widely in severity from benign to cancerous. Accurate diagnosis is essential for ensuring timely and appropriate treatment. This study examines the implementation of deep learning methods to assist in the diagnosis of skin lesions using the HAM10000 dataset, which contains seven distinct types of lesions. First, we evaluated three pre-trained models: MobileNetV2, ResNet18, and VGG11, achieving accuracies of 0.798, 0.802, and 0.805, respectively. To further enhance classification accuracy, we developed ensemble models employing max voting, average voting, and stacking, resulting in accuracies of 0.803, 0.82, and 0.83. Building on the best-performing ensemble learning model, stacking, we developed our proposed model, SkinNet, which incorporates a customized architecture and fine-tuning, achieving an accuracy of 0.867 and an AUC of 0.96. This substantial improvement over individual models demonstrates the effectiveness of ensemble learning in improving skin lesion classification.

Xiaoyi Liu, Zhou Yu, Lianghao Tan

Many people die from lung-related diseases every year. X-ray is an effective way to test if one is diagnosed with a lung-related disease or not. This study concentrates on categorizing three distinct types of lung X-rays: those depicting healthy lungs, those showing lung opacities, and those indicative of viral pneumonia. Accurately diagnosing the disease at an early phase is critical. In this paper, five different pre-trained models will be tested on the Lung X-ray Image Dataset. SqueezeNet, VGG11, ResNet18, DenseNet, and MobileNetV2 achieved accuracies of 0.64, 0.85, 0.87, 0.88, and 0.885, respectively. MobileNetV2, as the best-performing pre-trained model, will then be further analyzed as the base model. Eventually, our own model, MobileNet-Lung based on MobileNetV2, with fine-tuning and an additional layer of attention within feature layers, was invented to tackle the lung disease classification task and achieved an accuracy of 0.933. This result is significantly improved compared with all five pre-trained models.

Xiaoyi Liu, Ruina Du, Lianghao Tan et al.

Ensuring safety on construction sites is critical, with helmets playing a key role in reducing injuries. Traditional safety checks are labor-intensive and often insufficient. This study presents a computer vision-based solution using YOLO for real-time helmet detection, leveraging the SHEL5K dataset. Our proposed CIB-SE-YOLOv8 model incorporates SE attention mechanisms and modified C2f blocks, enhancing detection accuracy and efficiency. This model offers a more effective solution for promoting safety compliance on construction sites.