Detection of Occluded Small Commodities Based on Feature Enhancement under Super-Resolution

Dong, Haonan; Xie, Kai; Xie, An; Wen, Chang; He, Jing; Zhang, Wei; Yi, Dajiang; Yang, Shi Chun

doi:10.3390/s23052439

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…When performing merchandise detection in vision cabinets [2,3], it is usually necessary to feed the merchandise picked up by the consumer into the target detection network for detection. However, in this process, the consumer's rapid picking up of the merchandise can lead to motion blurring of the merchandise under fixed background conditions.…”

Section: Introductionmentioning

confidence: 99%

Improved Transformer-Based Deblurring of Commodity Videos in Dynamic Visual Cabinets

Huang,

Liang,

Xie

et al. 2024

Electronics

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In the dynamic visual cabinet, the occurrence of motion blur when consumers take out commodities will reduce the accuracy of commodity detection. Recently, although Transformer-based video deblurring networks have achieved results compared to Convolutional Neural Networks in some blurring scenarios, they are still challenging for the non-uniform blurring problem that occurs when consumers pick up the commodities, such as the problem of difficult alignment of blurred video frames of small commodities and the problem of underutilizing the effective information between the video frames of commodities. Therefore, an improved Transformer video deblurring network is proposed. Firstly, a multi-scale Transformer feature extraction method is utilized for non-uniform blurring. Secondly, for the problem of difficult alignment of small-item-blurred video frames, a temporal interactive attention mechanism is designed for video frame alignment. Finally, a feature recurrent fusion mechanism is introduced to supplement the effective information of commodity features. The experimental results show that the proposed method has practical significance in improving the accuracy of commodity detection. Moreover, compared with the recent Transformer deblurring algorithm Video Restoration Transformer, the Peak Signal-to-Noise Ratio of this paper’s algorithm is higher than that of the Deep Video Deblurring dataset and the Fuzzy Commodity Dataset by 0.23 dB and 0.81 dB, respectively.

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Section: Introductionmentioning

confidence: 99%

Improved Transformer-Based Deblurring of Commodity Videos in Dynamic Visual Cabinets

Huang,

Liang,

Xie

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Numerous videos are captured every day; however, due to the different capabilities of sensors, the quality of captured videos can vary greatly, which affects the subsequent analysis and applications [1][2][3][4]. Recently, computer technologies have been applied to many fields [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the adapted model gains the capability to exploit temporal information. (3) Extensive experiments are conducted to evaluate its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Adapting Single-Image Super-Resolution Models to Video Super-Resolution: A Plug-and-Play Approach

Wang

Liu

et al. 2023

Sensors

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The quality of videos varies due to the different capabilities of sensors. Video super-resolution (VSR) is a technology that improves the quality of captured video. However, the development of a VSR model is very costly. In this paper, we present a novel approach for adapting single-image super-resolution (SISR) models to the VSR task. To achieve this, we first summarize a common architecture of SISR models and perform a formal analysis of adaptation. Then, we propose an adaptation method that incorporates a plug-and-play temporal feature extraction module into existing SISR models. The proposed temporal feature extraction module consists of three submodules: offset estimation, spatial aggregation, and temporal aggregation. In the spatial aggregation submodule, the features obtained from the SISR model are aligned to the center frame based on the offset estimation results. The aligned features are fused in the temporal aggregation submodule. Finally, the fused temporal feature is fed to the SISR model for reconstruction. To evaluate the effectiveness of our method, we adapt five representative SISR models and evaluate these models on two popular benchmarks. The experiment results show the proposed method is effective on different SISR models. In particular, on the Vid4 benchmark, the VSR-adapted models achieve at least 1.26 dB and 0.067 improvement over the original SISR models in terms of PSNR and SSIM metrics, respectively. Additionally, these VSR-adapted models achieve better performance than the state-of-the-art VSR models.

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End-to-end multitasking network for smart container product positioning and segmentation

Shen,

Cai

2024

J. Electron. Imag.

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The current smart cooler's commodity identification system first locates the item being purchased, followed by feature extraction and matching. However, this method often suffers from inaccuracies due to the presence of background in the detection frame, leading to missed detections and misidentifications. To address these issues, we propose an end-to-end You Only Look Once (YOLO) for detection and segmentation algorithm. In the backbone network, we combine deformable convolution with a channel-to-pixel (C2f) module to enhance the model feature extraction capability. In the neck network, we introduce an optimized feature fusion structure, which is based on the weighted bi-directional feature pyramid. To further enhance the model's understanding of both global and local context, a triple feature encoding module is employed, seamlessly fusing multi-scale features for improved performance. The convolutional block attention module is connected to the improved C2f module to enhance the network's attention to the commodity image channel and spatial information. A supplementary segmentation branch is incorporated into the head of the network, allowing it to not only detect targets within the image but also generate precise segmentation masks for each detected object, thereby enhancing its multi-task capabilities. Compared with YOLOv8, for box and mask, the precision increases by 3% and 4.7%, recall increases by 2.8% and 4.7%, and mean average precision (mAP) increases by 4.9% and 14%. The frames per second is 119, which meets the demand for real-time detection. The results of comparative and ablation studies confirm the high accuracy and performance of the proposed algorithm, solidifying its foundation for fine-grained commodity identification.

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Detection of Occluded Small Commodities Based on Feature Enhancement under Super-Resolution

Cited by 3 publications

References 38 publications

Improved Transformer-Based Deblurring of Commodity Videos in Dynamic Visual Cabinets

Improved Transformer-Based Deblurring of Commodity Videos in Dynamic Visual Cabinets

Adapting Single-Image Super-Resolution Models to Video Super-Resolution: A Plug-and-Play Approach

End-to-end multitasking network for smart container product positioning and segmentation

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