A robust multiclass <scp>3D</scp> object recognition based on modern <scp>YOLO</scp> deep learning algorithms

Francies, Mariam Labib; Ata, Mohamed Maher; Mohamed, Mohamed A.

doi:10.1002/cpe.6517

Cited by 35 publications

(18 citation statements)

References 29 publications

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“…This means that there are as many AP values as there are classes. To obtain an estimate of the precision of the model overall for all classes, the mean value of APs of all classes is calculated and this metric is called the mean average precision ( mAP ) [ 32 ]. mAP is defined by the following expression and is one of the main metrics used to evaluate model performance:

where: mAP@α represents mean average precision; n represents number of classes; AP i represents average precision for a given class i.…”

Section: Methodsmentioning

confidence: 99%

Localization and Classification of Venusian Volcanoes Using Image Detection Algorithms

Đuranović

Šegota

Lorencin

et al. 2023

Sensors

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Imaging is one of the main tools of modern astronomy—many images are collected each day, and they must be processed. Processing such a large amount of images can be complex, time-consuming, and may require advanced tools. One of the techniques that may be employed is artificial intelligence (AI)-based image detection and classification. In this paper, the research is focused on developing such a system for the problem of the Magellan dataset, which contains 134 satellite images of Venus’s surface with individual volcanoes marked with circular labels. Volcanoes are classified into four classes depending on their features. In this paper, the authors apply the You-Only-Look-Once (YOLO) algorithm, which is based on a convolutional neural network (CNN). To apply this technique, the original labels are first converted into a suitable YOLO format. Then, due to the relatively small number of images in the dataset, deterministic augmentation techniques are applied. Hyperparameters of the YOLO network are tuned to achieve the best results, which are evaluated as mean average precision (mAP@0.5) for localization accuracy and F1 score for classification accuracy. The experimental results using cross-vallidation indicate that the proposed method achieved 0.835 mAP@0.5 and 0.826 F1 scores, respectively.

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where: mAP@α represents mean average precision; n represents number of classes; AP i represents average precision for a given class i.…”

Section: Methodsmentioning

confidence: 99%

Localization and Classification of Venusian Volcanoes Using Image Detection Algorithms

Đuranović

Šegota

Lorencin

et al. 2023

Sensors

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show abstract

“…They achieved a recognition accuracy of 96.82 % accuracy and a precision rate of 96.62 % for flame and smoke detection. (Jiao et al, 2019) made a deep learning-based forest fire detection system using Yolo V.3 (Francies et al, 2022). They Employed Conventional Neural Networks (CNN) (Bouwmans et al, 2019) Algorithm to train their model and installed it on their UAV.…”

Section: Prior Related Researchmentioning

confidence: 99%

Nature inspired firefighter assistant by unmanned aerial vehicle (UAV) data

Mousavi¹,

Ilanloo²

2023

10.5267/j.jfs

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One of the most hazardous phenomena in forests is wildfire or bush fire and early detection of massive damage prevention is vital. Employing Unmanned Aerial Vehicles (UAV) as a visual and extinguisher tool in order to prevent this tragedy which brings fatal effects on humans and wildlife has high importance. Additionally, using aerial imagery could assist firefighters to recognize fire intensity and localize and route the fire in the forest which shrinks down casualties of firefighters. All these benefits and more is just possible by employing cheap UAVs. The proposed research uses nature-inspired image processing techniques in order to segment and classify fire in color and thermal images. Multiple nature-inspired and traditional computer vision techniques, including Chicken Swarm Algorithm (CSA) intensity adjustment (contrast enhancement), Denoising Convolutional Neural Network (DnCNN), Local Phase Quantization (LPQ) feature extraction, Bees Image Segmentation, Biogeography-Based Optimization (BBO) feature selection, Firefly Algorithm (FA) classification and more are employed to achieve high classification and segmentation accuracy. The system evaluates nine performance metrics including, F-Score, Accuracy, and Jaccard for the segmentation stage and four performance metrics for the classification stage. All experiments are conducted on the two most recent UAV fire datasets of FLAME (2021) and DeepFire (2022). Additionally, fire intensity, fire direction, and fire geometrical calculation are calculated which assists firefighters even more. As smoke shows the location of the fire, a smoke detection workflow is proposed, too. Proposed system Compared with traditional and novel methods for segmentation and classification leading to satisfactory and promising results for almost all metrics. The trained model of this system could be used in most of the current rescue UAVs in real-time applications. For the FLAME dataset (color data), segmentation precision is 95.57 % and classification accuracy is 91.33 %. Also, For the DeepFire dataset segmentation precision is 91.74 % and classification accuracy is 96.88 %.

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“…The model was observed to improve prediction accuracy significantly without involving additional complex computations [15]. Thuan et al (2021) discussed the evolution of the YOLO algorithm and YOLOv5 as a state-of-the-art object detection algorithm [16]. The paper sheds light on how the YOLO algorithm was born to reframe the object detection problem as a regression problem, and it was carried out by a single neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The second component is the neck, which has series of layers that mix and combine image features and pass them forward for prediction. The third component, the head, collects the features from the neck and then draws a box around the object along with its predicted class [16]. The architecture of YOLOv5 is illustrated in Figure 3.…”

Section: Yolo Architecturementioning

confidence: 99%

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Classification of Malaria Using Object Detection Models

et al. 2022

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Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often time consuming and subject to error. Thus, the automated detection and classification of the malaria type and stage of progression can provide a quicker and more accurate diagnosis for patients. In this research, we used two object detection models, YOLOv5 and scaled YOLOv4, to classify the stage of progression and type of malaria parasite. We also used two different datasets for the classification of stage and parasite type while assessing the viability of the dataset for the task. The dataset used is comprised of microscopic images of red blood cells that were either parasitized or uninfected. The infected cells were classified based on two broad categories: the type of malarial parasite causing the infection and the stage of progression of the disease. The dataset was manually annotated using the LabelImg tool. The images were then augmented to enhance model training. Both models YOLOv5 and scaled YOLOv4 proved effective in classifying the type of parasite. Scaled YOLOv4 was in the lead with an accuracy of 83% followed by YOLOv5 with an accuracy of 78.5%. The proposed models may be useful for the medical professionals in the accurate diagnosis of malaria and its stage prediction.

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A robust multiclass 3D object recognition based on modern YOLO deep learning algorithms

Cited by 35 publications

References 29 publications

Localization and Classification of Venusian Volcanoes Using Image Detection Algorithms

Localization and Classification of Venusian Volcanoes Using Image Detection Algorithms

Nature inspired firefighter assistant by unmanned aerial vehicle (UAV) data

Classification of Malaria Using Object Detection Models

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