Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm

Pang, Lei; Liu, Hui; Chen, Yang; Miao, Jungang

doi:10.3390/s20061678

Cited by 69 publications

(37 citation statements)

References 34 publications

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“…e YOLOv3 algorithm is a typical one-stage object detection algorithm that combines the classification and target regression problems with an anchor box, thus achieving high efficiency, flexibility, and generalization performance [21]. Since the YOLOv3 was proposed, it has been used in various object detection tasks [22][23][24].…”

Section: Yolov3mentioning

confidence: 99%

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

Chen

et al. 2021

Computational Intelligence and Neuroscience

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Vision-based recognizing and positioning of electronic components on the PCB (printed circuit board) can improve the quality inspection efficiency of electronic products in the manufacturing process. With the improvement of the design and the production process, the electronic components on the PCB show the characteristics of small sizes and similar appearances, which brings challenges to visual object detection. This paper designs a real-time electronic component detection network through effective receptive field size and anchor size matching in YOLOv3. We make contributions in the following three aspects: (1) realizing the calculation and visualization of the effective receptive field size of the different depth layers of the CNN (convolutional neural network) based on gradient backpropagation; (2) proposing a modular YOLOv3 composition strategy that can be added and removed; and (3) designing a lightweight and efficient detection network by effective receptive field size and anchor size matching algorithm. Compared with the Faster-RCNN (regions with convolutional neural network) features, SSD (single-shot multibox detectors), and original YOLOv3, our method not only has the highest detection mAP (mean average precision) on the PCB electronic component dataset, which is 95.03%, the smallest parameter size of the memory, about 1/3 of the original YOLOv3 parameter amount, but also the second-best performance on FLOPs (floating point operations).

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Section: Yolov3mentioning

confidence: 99%

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

Chen

et al. 2021

Computational Intelligence and Neuroscience

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“…More specifically, two-stage detectors [ 32 , 33 , 34 , 35 , 36 , 37 ] are found to produce accurate detection outcomes. One-stage detectors [ 21 , 38 , 39 , 40 , 41 , 42 ] are introduced to gain computational efficiency. However, for moving object detection that involves high-resolution images, convolutional neural networks face several limitations [ 31 ], including (i) the inability to recognize motion and (ii) the generally much smaller input relative to high-resolution images of the size 1920 × 1080.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Moving Object Detection in High-Resolution Video Sensing

Zhu

Wei

et al. 2020

Sensors

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This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

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“…Yang et al [ 32 ] proposed a novel object detection method that combined multi-scale features and an attention-based rotation network. Pang et al [ 33 ] improved “you only look once” (YOLO) [ 34 ] to detect concealed objects. Yang et al [ 35 ] proposed a real-time cascaded framework to detect tiny faces.…”

Section: Related Workmentioning

confidence: 99%

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Leng

Yang

Kim

et al. 2020

Sensors

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Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.

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Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm

Cited by 69 publications

References 34 publications

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

Real-Time Moving Object Detection in High-Resolution Video Sensing

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

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