Rapid Earthquake Assessment from Satellite Imagery Using RPN and Yolo v3

Panday, Sanjeeb Prasad; Karn, Saurav Lal; Joshi, Basanta; Shakya, Aman; Pandey, Rom Kant

doi:10.1007/978-3-030-84522-3_23

Cited by 1 publication

(1 citation statement)

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“…For example, paper [6] conducted real-time detection of car collision damage using the YOLOv3 neural network, achieving detection accuracy of over 95%. Similarly, using the YOLOv3 neural network, the study in paper [7] performed change detection on damaged ground buildings using remote sensing images, yielding excellent results. The method based on semantic segmentation represents a further development of object detection methods (as shown in Figure 2).…”

Section: Introductionmentioning

confidence: 99%

Real-time damage process information detection method based on spatiotemporal attention neural network

ZHANG,

LOU,

ZHOU

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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Submunition swarm combat is a trend in modern battlefields. It aims to achieve precise and organized destruction of time-sensitive and mobile target groups in large operational depth, especially in GNSS-denied environments. This approach relies on assessing the target identification, positioning, and real-time damage assessment of submunition. After collecting damaged images, it is necessary to carry out damage information detection, including explosion flames, smoke, and other information, to determine the impact point of submunition and the process of damaging the target. However, when applying these methods to evaluate submunition, the performance of convolutional neural networks to extract target features still needs further improvement. This paper addresses the problem of false changes in images caused by projectile disturbances in complex backgrounds and the low accuracy of damage feature detection. Building upon the CosNet attention neural network, this paper uses an attention mechanism and proposes a damage feature extraction method based on spatiotemporal attention neural networks. This method achieves high-precision semantic segmentation of damage regions in continuous video sequences, providing a foundation for determining the impact point of submunition and assessing the damage effect. Through our simulation and experiment carried out by rocket sleds, the evaluation of submunition in orbital regions achieved realtime target identification and real-time extraction of the flare region, which validated the effectiveness of the spatiotemporal attention neural network in extracting damage regions in actual dynamic environments. This research provides a critical foundation for damage assessment, offering solutions that enhance the accuracy and reliability of realtime change detection in damage regions within high-dynamic environments.

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Section: Introductionmentioning

confidence: 99%