A Study on the Autonomous Detection of Impact Craters

Aburaed, Nour; Al-Saad, Mina; Mansoori, Saeed Al; Al-Ahmad, Hussain

doi:10.1007/978-3-031-20650-4_15

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…The CSP networks preserve DenseNet’s feature reuse qualities and reduce the redundant gradient information that normally occurs, which helps to increase the inference speed [ 29 ]. In the neck block, a modified version of the PANet (Path Aggregation Network) with C3 layers and the SPPF (Spatial Pyramid Pooling Fast) have been used [ 30 ]. The SPPF is an improved, faster version of the popular SPP with an increased flow of information, making it easier to locate pixels correctly.…”

Section: Methodsmentioning

confidence: 99%

YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System

Lindenheim-Locher,

Świtoński,

Krzeszowski

et al. 2023

Sensors

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This work is focused on the preliminary stage of the 3D drone tracking challenge, namely the precise detection of drones on images obtained from a synchronized multi-camera system. The YOLOv5 deep network with different input resolutions is trained and tested on the basis of real, multimodal data containing synchronized video sequences and precise motion capture data as a ground truth reference. The bounding boxes are determined based on the 3D position and orientation of an asymmetric cross attached to the top of the tracked object with known translation to the object’s center. The arms of the cross are identified by the markers registered by motion capture acquisition. Besides the classical mean average precision (mAP), a measure more adequate in the evaluation of detection performance in 3D tracking is proposed, namely the average distance between the centroids of matched references and detected drones, including false positive and false negative ratios. Moreover, the videos generated in the AirSim simulation platform were taken into account in both the training and testing stages.

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Section: Methodsmentioning

confidence: 99%

YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System

Lindenheim-Locher,

Świtoński,

Krzeszowski

et al. 2023

Sensors

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“…Rep-Pan Neck is considered more precise and operates faster than PANet and SPP. A convolutional layer has been added between the network and the final Head to improve performance, using processing power and memory [21][22]. Figure 3 shows the architecture of YOLOv6.…”

Section: Yolov6 Object Detection Modelmentioning

confidence: 99%

“…In [24], two models were compared in an oil tank dataset, showing mAP50-95 of 69.69 and 65.60% for YOLOv5-x and YOLOv6-l, respectively. In [21], the same models were compared on a lunar crater dataset, where OLOv5 achieved 72% mAP and YOLOv6 had 62% with SGD optimization. IV.…”

Section: Precision (P) =mentioning

confidence: 99%

Comparison of YOLOv5 and YOLOv6 Models for Plant Leaf Disease Detection

Iren

2024

Eng. Technol. Appl. Sci. Res.

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Deep learning is a concept of artificial neural networks and a subset of machine learning. It deals with algorithms that train and process datasets to make inferences for future samples, imitating the human process of learning from experiences. In this study, the YOLOv5 and YOLOv6 object detection models were compared on a plant dataset in terms of accuracy and time metrics. Each model was trained to obtain specific results in terms of mean Average Precision (mAP) and training time. There was no considerable difference in mAP between both models, as their results were close. YOLOv5, having 63.5% mAP, slightly outperformed YOLOv6, while YOLOv6, having 49.6% mAP50-95, was better in detection than YOLOv5. Furthermore, YOLOv5 trained data in a shorter time than YOLOv6, since it has fewer parameters.

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“…The training technique also incorporates an anchor-free paradigm, SimOTA label assignment policy, and Scale-Sensitive IOU (SIOU) Bounding Box regression loss for effective inferencing. [25]. The overall architecture of YOLOv6 is shown in Fig.…”

Section: B Yolov6mentioning

confidence: 99%

Investigation of You Only Look Once Networks for Vision-based Small Object Detection

Yang¹

2023

IJACSA

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Small object detection is a challenging issue in computer vision-based algorithms. Although various methods have been investigated for common objects including person, car and others, small object are not addressed in this issue. Therefore, it is necessary to conduct more researches on them. This paper is focused on small object detection especially jewellery as current object detection methods suffer from low accuracy in this domain. This paper introduces a new dataset whose images were taken by a web camera from a jewellery store and data augmentation procedure. It comprises three classes, namely, ring, earrings, and pendant. In view of the small target of jewellery and the real-time detection, this study adopted the You Only Look Once (Yolo) algorithms. Different Yolo based model including eight versions are implemented and train them using our dataset to address most effective one. Evaluation criteria, including accuracy, F1 score, recall, and mAP, are used to evaluate the performance of the various YOLOv5, YOLOv6, and YOLOv7 versions. According to the experimental findings, utilizing YOLOv6 is significantly superior to YOLOv7 and marginally superior to YOLOv5.

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A Study on the Autonomous Detection of Impact Craters

Cited by 9 publications

References 38 publications

YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System

YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System

Comparison of YOLOv5 and YOLOv6 Models for Plant Leaf Disease Detection

Investigation of You Only Look Once Networks for Vision-based Small Object Detection

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