Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey

Papadeas, Ilias; Tsochatzidis, Lazaros; Amanatiadis, Angelos; Pratikakis, Ioannis

doi:10.3390/app11198802

Cited by 36 publications

(14 citation statements)

References 59 publications

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“…The more processed frames per second, the faster the model infers. The FPS is found by calculating the reciprocal of the inference time (Equation (12)) [ 38 ].

…”

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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“…The more processed frames per second, the faster the model infers. The FPS is found by calculating the reciprocal of the inference time (Equation (12)) [ 38 ].

…”

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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“…Frames per second (FPS) [50] is a metric used to measure the model's detection speed, and it is defined as the reciprocal of the inference time. FPS refers to the number of images that can be detected per second.…”

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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“…Semantic image segmentation entails classifying every pixel of a picture with a class that corresponds to what it depicts [78]. This task is commonly known as dense prediction because we are predicting for every pixel within the photo.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Object-based Classification of Natural Scenes Using Machine Learning Methods

Jasim¹,

Younis²

2023

Technium

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The replication of human intellectual processes by machines, particularly computer systems, is known as artificial intelligence (AI). AI is an intelligent tool that is utilized across sectors to improve decision making, increase productivity, and eliminate repetitive tasks. Machine learning (ML) is a key component of AI since it includes understanding and developing ways that can learn or improve performance on tasks. For the last decade, ML has been applied in computer vision (CV) applications. In computer vision, systems and computers extract meaningful data from digital videos, photos, and other visual sources and use that information to conduct actions or make suggestions. In this work, we have solved the image segmentation problem for the natural images to segment out water, land, and sky. Instead of applying image segmentation directly to the images, images are pre-processed, and statistical and textural features are then passed through a neural network for the pixel-wise semantic segmentation of the images. We chose the 5X5 window over the pixel-by-pixel technique since it requires less resources and time for training and testing.

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Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey

Cited by 36 publications

References 59 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

Object-based Classification of Natural Scenes Using Machine Learning Methods

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