Hybrid features for object detection in RGB-D scenes

Awwad, Sari; Igried, Bashar; Wedyan, Mohammad; Alshirah, Mohammad

doi:10.11591/ijeecs.v23.i2.pp1073-1083

Cited by 2 publications

(2 citation statements)

References 29 publications

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“…The F1-score is calculated from the precision and recall. The F1-score is a machine learning evaluation metric that measures the model’s accuracy by taking into account both the false positives and false negatives of the tested model, and is calculated as shown in Equation (10) [ 37 ].

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Section: Resultsmentioning

confidence: 99%

Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach

Ghourabi

Mourad-Chehade

Chkeir

2023

Sensors

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Early detection of physical frailty and infectious diseases in seniors is important to avoid any fatal drawback and promptly provide them with the necessary healthcare. One of the major symptoms of viral infections is elevated body temperature. In this work, preparation and implementation of multi-age thermal faces dataset is done to train different “You Only Look Once” (YOLO) object detection models (YOLOv5,6 and 7) for eye detection. Eye detection allows scanning for the most accurate temperature in the face, which is the inner canthus temperature. An approach using an elderly thermal dataset is performed in order to produce an eye detection model specifically for elderly people. An application of transfer learning is applied from a multi-age YOLOv7 model to an elderly YOLOv7 model. The comparison of speed, accuracy, and size between the trained models shows that the YOLOv7 model performed the best (Mean average precision at Intersection over Union of 0.5 (mAP@.5) = 0.996 and Frames per Seconds (FPS) = 150). The bounding box of eyes is scanned for the highest temperature, resulting in a normalized error distance of 0.03. This work presents a fast and reliable temperature detection model generated using non-contact infrared camera and a deep learning approach.

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…”

Section: Resultsmentioning

confidence: 99%

Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach

Ghourabi

Mourad-Chehade

Chkeir

2023

Sensors

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show abstract

“…Specifically, precision measures the accuracy of the algorithm, while recall measures the completeness of the image recognition results. The F1-score [48] is a composite evaluation metric for model detection accuracy and is the harmonic mean of precision and recall. The specific definitions of these evaluation metrics are as follows:…”

Section: Accuracy Evaluation Indexmentioning

confidence: 99%

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

Meng,

Li,

et al. 2023

Remote Sensing

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Detection and counting of wheat heads are crucial for wheat yield estimation. To address the issues of overlapping and small volumes of wheat heads on complex backgrounds, this paper proposes the YOLOv7-MA model. By introducing micro-scale detection layers and the convolutional block attention module, the model enhances the target information of wheat heads and weakens the background information, thereby strengthening its ability to detect small wheat heads and improving the detection performance. Experimental results indicate that after being trained and tested on the Global Wheat Head Dataset 2021, the YOLOv7-MA model achieves a mean average precision (MAP) of 93.86% with a detection speed of 35.93 frames per second (FPS), outperforming Faster-RCNN, YOLOv5, YOLOX, and YOLOv7 models. Meanwhile, when tested under the three conditions of low illumination, blur, and occlusion, the coefficient of determination (R2) of YOLOv7-MA is respectively 0.9895, 0.9872, and 0.9882, and the correlation between the predicted wheat head number and the manual counting result is stronger than others. In addition, when the YOLOv7-MA model is transferred to field-collected wheat head datasets, it maintains high performance with MAP in maturity and filling stages of 93.33% and 93.03%, respectively, and R2 values of 0.9632 and 0.9155, respectively, demonstrating better performance in the maturity stage. Overall, YOLOv7-MA has achieved accurate identification and counting of wheat heads in complex field backgrounds. In the future, its application with unmanned aerial vehicles (UAVs) can provide technical support for large-scale wheat yield estimation in the field.

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Hybrid features for object detection in RGB-D scenes

Cited by 2 publications

References 29 publications

Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach

Eye Recognition by YOLO for Inner Canthus Temperature Detection in the Elderly Using a Transfer Learning Approach

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

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