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Improving Deep Learning-based Automatic Checkout System Using image Enhancement Techniques

Improving Deep Learning-based Automatic Checkout System Usin...

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2023 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Pham, Long Hoang Tran, Duong Nguyen-Ngoc Nguyen, Huy-Hung Jeon, Hyung-Joon Tran, Tai Huu-Phuong Jeon, Hyung-Min Jeon, Jae Wook Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350302493

The retail sector has experienced significant growth in artificial intelligence and computer vision applications, particularly with the emergence of automatic checkout (ACO) systems in stores and supermarkets. ACO systems encounter challenges such as object occlusion, motion blur, and similarity between scanned items while acquiring accurate training images for realistic checkout scenarios is difficult due to constant product updates. This paper improves existing deep learning-based ACO solutions by incorporating several image enhancement techniques in the data pre-processing.step. The proposed ACO system employs a detect-and-track strategy, which involves: (1) detecting objects in areas of interest;(2) tracking objects in consecutive frames;and (3) counting objects using a track management pipeline. Several data generation techniques - including copy-and-paste, random placement, and augmentation - are employed to create diverse training data. Additionally, the proposed solution is designed as an open-ended framework that can be easily expanded to accommodate multiple tasks. The system has been evaluated on the AI City Challenge 2023 Track 4 dataset, showcasing outstanding performance by achieving a top-1 ranking on test-set A with an F1 score of 0.9792. © 2023 ieee.

关键词： image enhancement

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MLNet: Mutual Learning Network to Improve Self-Supervised Representation for Fine-Grained Visual recognition

MLNet: Mutual Learning Network to Improve Self-Supervised Re...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Zhao, Peipei Wang, Jiaxuan Lu, Zixiang Miao, Qiguang School of Computer Science and Technology Xidian University Xi'an China

ISBN: (纸本)9798350368741

High-quality annotation of fine-grained visual categorization requires extensive professional knowledge, which is time-consuming and laborious. Therefore, learning fine-grained visual representations from a large number of unlabeled images through self-supervised learning has become a popular alternative solution recently. However, the existing self-supervised learning methods are not effective in fine-grained visual categorization since many features helpful for optimizing self-supervised learning objectives are unsuited to characterize the subtle differences in fine-grained viusal recognition. To deal with this issue, we propose a mutual learning network to enhance the model's attention towards discriminative semantic features. The key idea is to consider semantic consistency between different augmented views within same image and capture discriminative semantic information. For semantic consistency, our research demonstrates that cross-view attention module between different augmented views can guide our model to capture similar semantic features. Based on this, we further build a GradCAM-guided multi-dimension loss that utilize GradCAM to control our model from different dimensions to discriminative semantic information that are beneficial to fine-grained visual recognition. Experiments on CUB-200-2011, Stanford Cars and Aircrafts datasets demonstrate that the mutual learning network outperforms previous self-supervised learning methods in linear probing and image retrieval. © 2025 ieee.

关键词： cross-view attention module fine-grained visual categorization GradCAM-guided multi-dimension loss self-supervised learning

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A new RCNN-RF for efficient and robust parking lot available detection 16

A new RCNN-RF for efficient and robust parking lot available...

引用

16th International conference on Digital image processing. ICDIP 2024

作者： Zhang, Qian Ran, Yaqin Luo, Di Xu, Kaili Shi, Jilaing School of Data Science and Information Engineering GuiZhou MinZu University Guiyang550025 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang550025 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

It's very meaningful to conduct the driver to the parking space available in the parking lot clearly and accurately by computer vision and computational intelligence. While it is an extremely difficult task, because of the scene complexity in the parking lot. In this paper, a new computational intelligence technology is proposed to detect parking spaces available in the parking lot outdoor based on fusing of Region Convolution Neural Network (RCNN) and random forests (RF), and we called it as RCNN-RF. Within the RCNN-RF, a new activation function is design. And with the help of random forests, RCNN-RF overcomes scene complexity includes illumination, weather, and slight occlusion successfully. Extensive experimental results on the benchmark dataset named PKLot and the self-built dataset named GZMU-LOT outperform those of the state-of-the-art, and it shows the superiority of our proposed RCNN-RF method. © 2024 SPIE.

关键词： Random forests

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On Energy-aware and Verifiable Benchmarking of Big Data processing.targeting AI Pipelines

On Energy-aware and Verifiable Benchmarking of Big Data Proc...

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2024 ieee International conference on Big Data, BigData 2024

作者： Theodorou, Georgios Karagiorgou, Sophia Kotronis, Christos Ubitech Ltd Limassol Cyprus

ISBN: (纸本)9798350362480

As Artificial Intelligence (AI) is revolutionizing various industries and applications, understanding the hardware requirements and energy consumption of AI pipelines in Big Data (BD) applications has become increasingly essential. This paper presents a comprehensive, scalable framework, designed to systematically measure hardware resources, energy usage, and model performance across two prominent data modalities: tabular data and images. The framework is generalizable, facilitating replicability across the AI research community, and encourages the deployment of AI models with comprehensive metrics beyond traditional accuracy, promoting the optimization of pipelines for real-world scenarios. Through detailed benchmarking, we identify EfficientNet as a standout model for image classification, and XGBoost for tabular data, both excelling in their respective domains. Notably, our findings show that Graphics processing.Units (GPUs) account for approximately 90% of total energy consumption in image-based tasks, while Central processing.Units (CPUs) are responsible for around 50% of energy use in tabular data processing. The merit of our innovative proposed framework combines information theory and probability theory to enhance our understanding of AI model performance in Edge-to-Cloud (E2C) applications that demand efficient Big Data processing.in distributed environments. By seamlessly integrating energy efficiency with hardware optimization, it enables realtime monitoring of energy consumption and computing resources in containerized environments, providing precise insights for optimizing AI workloads. This framework facilitates scalable AI deployment on resource-constrained edge devices, reducing energy consumption while enhancing AI model robustness and interpretability, thereby promoting greater trust and transparency in AI-powered decision-making for critical real-world applications. This emphasizes the importance of multi-objective optimization for more sustainabl

关键词： Graphics processing.unit

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Feature Selection Using Evolutionary Techniques

Feature Selection Using Evolutionary Techniques

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2023 ieee International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gholami, Mandana Mouhoub, Malek Sadaoui, Samira University of Regina Dept. of Computer Science ReginaSKS4S 0A2 Canada

ISBN: (纸本)9798350337020

Data clustering has many applications in machine learning, data mining and image processing. K-means is the most popular clustering algorithm due to its efficiency and simplicity of implementation. However, K-means has limitations, such as large feature spaces, which may affect its effectiveness. To improve K-means accuracy, we adopt the Biogeography-Based Optimization (BBO) evolutionary technique to select the most relevant features of datasets. We conducted several experiments to compare our approach with other methods, such as PCA and Particle Swarm Optimization (PSO). The results demonstrate the effectiveness of BBO for feature selection. © 2023 ieee.

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Comprehensive Visual Features and Pseudo Labeling for Robust Natural Language-based Vehicle Retrieval

Comprehensive Visual Features and Pseudo Labeling for Robust...

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2023 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Ngo, Bach Hoang Nguyen, Dat Thanh Do-Tran, Nhat-Tuong Huy Thien, Phuc Pham An, Minh-Hung Nguyen, Tuan-Ngoc Hoang, Loi Nguyen Dinh Nguyen, Vinh Dinh, Quang-Vinh Ai Vietnam Research Viet Nam Fpt Telecom Viet Nam Ho Chi Minh City University of Technology Faculty of Computer Science and Engineering Viet Nam Eastern International University School of Computing and Information Technology Viet Nam

ISBN: (纸本)9798350302493

Vehicle retrieval has become crucial for public transportation and intelligent transportation systems due to the exponential development of large-scale transportation videos. Vehicle re-identification and vehicle tracking are the main components of most vision-based vehicle recovery systems. Unfortunately, the limited amount of information provided by traffic video feeds limits the vision-based vehicle retrieval algorithm's efficacy. Therefore, this article proposes a contrastive cross-modal vehicle retrieval approach to maximize the complementarity of natural language and visual representations. An efficient method to fuse multiple input image features is also proposed to extract comprehensive information from various vehicles along with pseudo labeling and efficient post-processing.techniques to enhance retrieval accuracy. The proposed method achieved the 3rd ranking of Mean Reciprocal Rank (MRR) score of 0.4795 on the test set for the Challenge Track 2: Tracked-Vehicle Retrieval by Natural Language Descriptions 2023. Source code for the proposed approaches is openly accessible at https://***/anminhhung/AICity-2023-Track2. © 2023 ieee.

关键词： image enhancement

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Application of subspace-based CNN speech noise reduction model in medical field

Application of subspace-based CNN speech noise reduction mod...

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Fu, Xian Dong, Sisi Zhou, Weiqi Zhang, Zhuzhu Ye, Ruiwen Ren, Ting School of Computer and Information Engineering Hubei Normal University Huangshi435000 China

ISBN: (纸本)9781510666351

In order to enhance the noise reduction capability and recognition efficiency of speech recognition technology applied in medical devices, this paper adopts an improved subspace CNN based noise reduction model, adding a subspace projection module with two orthogonal self-attentive mechanisms to the original model, inputting the feature vectors extracted from the convolutional layer to the self-attentive layer, and the two self-attentive layers learn and project orthogonally to each other to obtain the noise embedding vector and speech embedding vectors to extract cleaner speech. In this paper, the improved subspace CNN noise reduction model is compared with the CNN noise reduction model by comparing the distortion degree, segmental signal-to-noise ratio and quality at different signal-to-noise ratios to demonstrate the reliability of the model. The results show that the overall distortion level of the model is reduced by about 7.27%, the overall segmental SNR performance is increased by about 15.27% and the quality is improved by about 8.7%, which is a certain improvement in performance compared with the traditional CNN noise reduction model. © 2023 SPIE.

关键词： Vectors

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Lightweight target detection method based on improved YOLOv5

Lightweight target detection method based on improved YOLOv5

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Feng, Jianxin Zhou, Minghao Hao, Enguang Pan, Chengsheng Liaoning Key Laboratory of Communication Network Liaoning Dalian116622 China College of Information Engineering Dalian University Liaoning Dalian116622 China

ISBN: (纸本)9781510666351

Target detection is one of the hot research issues in the field of computer vision in recent years. Large scale convolution networks can effectively improve the accuracy of target detection, but they are not suitable for application scenarios with limited computing and storage capacity. In order to solve the above problems, this paper proposes a lightweight target detection network based on improved YOLOv5 - CS-YOLOv5 network. This network uses a lightweight network ShuffleNetv2 to reconstruct backbone, effectively reducing the network complexity, and adds a CA attention module to the feature extraction network to enhance the network feature expression ability, and solves the problem of neuron deactivation through H-Swish activation function. The final model size was compressed to 1.64MB, and the mAP50 on the COCO test set reached 47.6%, reducing the model size by 13.7% and improving the detection accuracy by 1.9% compared with the original YOLOv5 model. © 2023 SPIE.

关键词： image processing

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In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient computer Vision 48

In-Sensor & Neuromorphic Computing Are all You Need for Ener...

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48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Datta, Gourav Liu, Zeyu Kaiser, Md Abdullah-Al Kundu, Souvik Mathai, Joe Yin, Zihan Jacob, Ajey P. Jaiswal, Akhilesh R. Beerel, Peter A. University of Southern California United States Information Sciences Institute United States Intel Labs United States

ISBN: (纸本)9781728163277

Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for several computer vision (CV) applications. However, most existing SNNs require multiple time steps for acceptable inference accuracy, hindering real-time deployment and increasing spiking activity and, consequently, energy consumption. Recent works proposed direct encoding that directly feeds the analog pixel values in the first layer of the SNN in order to significantly reduce the number of time steps. Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing.is efficient using SNNs, the data transfer between the image sensors and the downstream processing.costs significant bandwidth and may dominate the total energy. To mitigate this concern, we propose an in-sensor computing hardware-software co-design framework for SNNs targeting image recognition tasks. Our approach reduces the bandwidth between sensing and processing.by 12-96× and the resulting total energy by 2.32× compared to traditional CV processing. with a 3.8% reduction in accuracy on imageNet. © 2023 ieee.

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ILDiff: Generate Transparent Animated Stickers by Implicit Layout Distillation

ILDiff: Generate Transparent Animated Stickers by Implicit L...

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2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Zhang, Ting Yuan, Zhiqiang Zhu, Yeshuang Zhou, Jie Zhang, Jinchao Pattern Recognition Center WeChat AI Tencent China

ISBN: (纸本)9798350368741

High-quality animated stickers usually contain transparent channels, which are often ignored by current video generation models. To generate fine-grained animated transparency channels, existing methods can be roughly divided into video matting algorithms and diffusion-based algorithms. The methods based on video matting have poor performance in dealing with semi-open areas in stickers, while diffusion-based methods are often used to model a single image, which will lead to local flicker when modeling animated stickers. In this paper, we firstly propose an ILDiff method to generate animated transparent channels through implicit layout distillation, which solves the problems of semi-open area collapse and no consideration of temporal information in existing methods. Secondly, we create the Transparent Animated Sticker Dataset (TASD), which contains 0.32M high-quality samples with transparent channel, to provide data support for related fields. Extensive experiments demonstrate that ILDiff can produce finer and smoother transparent channels compared to other methods such as Matting Anything and Layer Diffusion. Our code and dataset will be released at link https://***. © 2025 ieee.

关键词： animated sticker generation implicit layout distillation transparent channel generation

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