Target aided identification in passive human THz-image

Ran, 赵然 Zhao; Yuanmeng, 赵源萌 Zhao; Cunlin, 张存林 Zhang

doi:10.3788/hplpb20142607.73102

Cited by 2 publications

(2 citation statements)

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“…Radar, ultrasonic sensor and CMOS camera are integrated into one. The advantage of this integration not only improves the accuracy and reliability of mobile robot identification of indoor locations, but also can continue to identify under various conditions to achieve the purpose of identification [6]; because the CMOS camera requires There are requirements for light intensity. It can only play a role in the condition of sufficient light to visually recognize objects.…”

Section: Iceeat-2022 Journal Of Physics: Conference Series 2467 (2023...mentioning

confidence: 99%

Indoor location recognition device and method based on indoor robot

Wang

Mokhtarzadeh

2023

J. Phys.: Conf. Ser.

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This paper describes an indoor location recognition device and method based on an indoor robot. The device includes a computer Raspberry Pi, a lidar, a CMOS camera, an ultrasonic sensor, a first microcontroller, and a servo motor; the lidar is connected to the computer Raspberry Pi. Pie, the lidar is driven by a servo motor, the ultrasonic sensor is connected to the computer Raspberry Pi through the first microcontroller, and the computer Raspberry Pi controls the lidar to scan the location to measure the width and length of the current location, and controls the ultrasonic sensor to the location. Perform up and down scanning to measure the distance between the ultrasonic sensor and the ground and the ceiling respectively, and obtain the height of the room; the CMOS camera is connected to the computer Raspberry Pi, and the computer Raspberry Pi sends an instruction to control the CMOS camera to take pictures of objects and places and use them Opencv and other related technologies achieve the purpose of identification. The device and method can continuously identify indoor locations under various complex conditions, and at the same time improve the accuracy and reliability of location recognition.

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Section: Iceeat-2022 Journal Of Physics: Conference Series 2467 (2023...mentioning

confidence: 99%

Indoor location recognition device and method based on indoor robot

Wang

Mokhtarzadeh

2023

J. Phys.: Conf. Ser.

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“…Therefore, extracting features of targets from terahertz images and automatically and accurately identifying and locating various types of targets is the key to efficient detection. Traditional target detection methods for terahertz images include threshold segmentation methods [7], edge-based detection methods [8], and clusteringbased detection methods [9]. However, the traditional terahertz image detection algorithms are not effective because terahertz images lack texture details, the target contour is not obvious, and it is more likely to produce misdetection and omission detection in complex background environments.…”

Section: Introductionmentioning

confidence: 99%

Lightweight YOLOv7 Algorithm for Multi-Object Recognition on Contrabands in Terahertz Images

Ge,

Zhang,

Jiang

et al. 2024

Applied Sciences

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With the strengthening of worldwide counter-terrorism initiatives, it is increasingly important to detect contrabands such as controlled knives and flammable materials hidden in clothes and bags. Terahertz (THz) imaging technology is widely used in the field of contraband detection due to its advantages of high imaging speed and strong penetration. However, the terahertz images are of poor qualities and lack texture details. Traditional target detection methods suffer from low detection speeds, misdetection, and omission of contraband. This work pre-processes the original dataset using a variety of image processing methods and validates the effect of these methods on the detection results of YOLOv7. Meanwhile, the lightweight and multi-object detection YOLOv7 (LWMD-YOLOv7) algorithm is proposed. Firstly, to meet the demand of real-time for multi-target detection, we propose the space-to-depth mobile (SPD_Mobile) network as the lightweight feature extraction network. Secondly, the selective attention module large selective kernel (LSK) network is integrated into the output of the multi-scale feature map of the LWMD-YOLOv7 network, which enhances the effect of feature fusion and strengthens the network’s attention to salient features. Finally, Distance Intersection over Union (DIOU) is used as the loss function to accelerate the convergence of the model and to have a better localisation effect for small targets. The experimental results show that the YOLOv7 algorithm achieves the best detection results on the terahertz image dataset after the non-local mean filtering process. The LWMD-YOLOv7 algorithm achieves a detection accuracy P of 98.5%, a recall R of 97.5%, and a detection speed of 112.4 FPS, which is 26.9 FPS higher than that of the YOLOv7 base network. The LWMD-YOLOv7 achieves a better balance between detection accuracy and detection speed. It provides a technological reference for the automated detection of contraband in terahertz images.

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Target aided identification in passive human THz-image

Cited by 2 publications

References 0 publications

Indoor location recognition device and method based on indoor robot

Indoor location recognition device and method based on indoor robot

Lightweight YOLOv7 Algorithm for Multi-Object Recognition on Contrabands in Terahertz Images

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