Toward New Retail: A Benchmark Dataset for Smart Unmanned Vending Machines

Zhang, Haijun; Li, Donghai; Ji, Yuzhu; Zhou, Haibin; Wu, Weiwei; Li, Kai

doi:10.1109/tii.2019.2954956

Cited by 42 publications

(18 citation statements)

References 23 publications

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“…In this paper, the detection tasks are divided into three categories: serial number 0 corresponds to the “face”, indicating that no mask is worn; serial number 1 is equal to “face_mask”, showing that the face wears a mask regularly; and serial number 2 is equivalent to “WMI”, which means wearing masks irregularly. The sample distribution of different categories in the data set is shown in Table 2 , where images represent the number of categories and objects represent the number of instances [ 41 ].…”

Section: Experimental Data Setmentioning

confidence: 99%

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4

Zhang

2021

Sensors

220

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To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm.

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Section: Experimental Data Setmentioning

confidence: 99%

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4

Zhang

2021

Sensors

220

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“…As it could be seen, on PASCAL VOC benchmark, the baseline detector helps improve a slight point when employed with our SAFS module. In addition, on another object detection dataset UVM [39], we could also find that SAFS module contributes to an obvious improvement on total AP metric, especially AP under IoU 0.75. This could conclude that our method shows to be a good way to detect objects in a finer level.…”

Section: Generalization Capacitymentioning

confidence: 75%

“…In this section, we conduct experiments on the COCO dataset [29] as well as PASCAL VOC [38] and UVM [39] dataset. Following the general training strategy for COCO, we train our models with the union of 80k training images and 35k subset of validation images (trainval).…”

Section: Optimizationmentioning

confidence: 99%

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Learning region-guided scale-aware feature selection for object detection

Liu

Wang

Xie

et al. 2020

Neural Comput & Applic

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Scale variation is one of the major challenges in object detection task. Modern region-based object detection architectures often adopt Feature Pyramid Network (FPN) as feature extraction neck to achieve multi-scale feature representation in solving scale variation problem. However, due to the rough feature selection strategy in Region of Interest (RoI) feature extraction step, these methods might not perform well on object detection under strong scale variation. In this work, we are motivated by the limitations of current FPN-based two-stage object detectors and then present a novel module, namely scale-aware feature selective (SAFS) module, that flexibly and adaptively selects feature levels in two-stage object detectors. Specifically, we firstly build the RoI Pyramid in standard FPN structure to extract RoI features from various scale levels. Next, in order to achieve scale-aware mechanism for solving scale variation issue, we develop a novel weighting gate function containing one set of trainable parameters to automatically learn the fusion weight for each RoI feature level, which relieves the limitation of hard feature selection strategy guided by online instance size. Outputs from the RoI features with the learned weights are fused for classification and bounding box regression. Furthermore, we design a multilevel SAFS architecture to obtain different types of RoI feature combinations that ensures our method is more robust to various instance scales. Experimental results show that our SAFS module is very compatible with most of two-stage object detectors and could achieve state-of-the-art results with Average Precision of 48.3 on COCO test-dev and other popular object detection benchmarks. Our code will be made publicly available.

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“…This involves both the ability to recognize (i.e. classify) the objects, and locating the recognized objects in the input image [13]. The current object detection methods can be divided into five main categories based on image segmentation [14]- [15], template matching [16], optical flow [17]- [18], frame difference [19]- [20], and machine learning (including both traditional machine learning and deep learning).…”

Section: B Object Detection Technologymentioning

confidence: 99%

Bucket Teeth Detection Based on Faster Region Convolutional Neural Network

Zhang

et al. 2021

IEEE Access

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The electric shovel is a bucket-equipped mining excavator widely used in open-pit mining today. The prolonged direct impact of the bucket teeth with hard and abrasive materials such as ore during the process of the mining excavation can cause the bucket teeth to break and fall off prematurely, resulting in unplanned downtime and productivity losses. In response to this problem, we have developed a visionbased bucket teeth fault detection algorithm with deep learning. Using a dataset based on the images of both real shovel teeth and 3D-printed models, we trained a Faster Region Convolutional Neural Network (Faster R-CNN) to obtain the number of normal bucket teeth and the positions of the bucket teeth from the images, using the additional bucket dataset from 3D-printed models to pre-train the network for improving its detection accuracy on the real bucket data. We compared the resulting Faster R-CNN model with the ZFNet, the ResNet-50, and the VGG16 and found our Faster R-CNN model to perform best in terms of accuracy and speed.

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Toward New Retail: A Benchmark Dataset for Smart Unmanned Vending Machines

Cited by 42 publications

References 23 publications

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4

Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4

Learning region-guided scale-aware feature selection for object detection

Bucket Teeth Detection Based on Faster Region Convolutional Neural Network

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