Automated Target Detection for Geophysical Applications

Peer, Uri; Dy, Jennifer G.

doi:10.1109/tgrs.2016.2627245

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…One such approach has been successfully used for finding spatio-temporal patterns in sea surface height data [36], [37], resulting in the creation of a global catalogue of mesoscale ocean eddies [38]. Another approach for finding anomalous objects buried under the surface of the Earth (e.g., land mines) from radar images was explored in [39], using unsupervised techniques that can work with mediums of varying properties. The use of topic models has also been explored for finding extreme events from climate time series data [40].…”

Section: Characterizing Objects and Eventsmentioning

confidence: 99%

Machine Learning for the Geosciences: Challenges and Opportunities

Karpatne

Ebert‐Uphoff

Ravela

et al. 2019

IEEE Trans. Knowl. Data Eng.

392

197

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Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. As geosciences enters the era of big data, machine learning (ML)-that has been widely successful in commercial domains-offers immense potential to contribute to problems in geosciences. However, problems in geosciences have several unique challenges that are seldom found in traditional applications, requiring novel problem formulations and methodologies in machine learning. This article introduces researchers in the machine learning (ML) community to these challenges offered by geoscience problems and the opportunities that exist for advancing both machine learning and geosciences. We first highlight typical sources of geoscience data and describe their properties that make it challenging to use traditional machine learning techniques. We then describe some of the common categories of geoscience problems where machine learning can play a role, and discuss some of the existing efforts and promising directions for methodological development in machine learning. We conclude by discussing some of the emerging research themes in machine learning that are applicable across all problems in the geosciences, and the importance of a deep collaboration between machine learning and geosciences for synergistic advancements in both disciplines.

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Section: Characterizing Objects and Eventsmentioning

confidence: 99%

Machine Learning for the Geosciences: Challenges and Opportunities

Karpatne

Ebert‐Uphoff

Ravela

et al. 2019

IEEE Trans. Knowl. Data Eng.

392

197

View full text Add to dashboard Cite

show abstract

“…With the continuous development of artificial intelligence technology, image recognition and target detection have been gradually applied to a variety of fields, such as industrial, military, commercial, and medical [5]. A particular application is the use of image processing technology to achieve the diagnosis of agricultural pests.…”

Section: Introductionmentioning

confidence: 99%

Identification Method of Crop Aphids Based on Bionic Attention

Hu,

Li,

et al. 2024

Agronomy

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The accurate identification of crop aphids is an important aspect of improving agricultural productivity. Aphids are characterised by small targets and a body colour similar to their surroundings. Even the most advanced detectors can experience problems such as low detection accuracy and a high number of missed detections. In this paper, a multi-stream target detection model is proposed for fast and accurate detection of crop aphids in complex backgrounds. First, inspired by the human visual system, we propose a bionic attention (BA) approach. Unlike previous strategies, we do not improve the model but input additional category labels as bionic information streams into the network at the network input stage to support mainstream recognition, which improves the detection effect. In addition, through web crawling and manual screening, we construct an aphid dataset containing 2059 images, named IP_Aphids, based on IP102. In this paper, we combine the proposed BA with a number of classical target detection models, including YOLOv5s, YOLOv7-tiny, YOLOv8n, SSD, and faster R-CNN, and we also compare the BA with the classical attention mechanisms CBAM and SE. This approach allows the effectiveness of the method to be evaluated from multiple perspectives. The results show that combining the bionic information flow of BA with the original mainstream information flow as inputs to the five target detection models improves the mAP by 2.2%, 7%, 2.7%, 1.78%, and 1.12%, respectively, compared to the models using only the original inputs. In addition, the mAP of the YOLOv8n_BA model is higher than that of the YOLOv8n, YOLOv8n_CBAM, and YOLOv8n_SE models by 4.6%, 3.3%, and 2.7%, respectively. This indicates that the BA proposed in this study is significantly better than the classical attention to improve crop aphid detection, which provides a reference for crop aphid-related research.

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“…However, the Hough transform is computationally expensive. To reduce computational cost, some researchers have used template matching and edge detection to fit hyperbolas in GPR B-scans [14], [15]. However, these methods typically only apply to identify hyperbolas with relatively regular reflection signals.…”

Section: Introductionmentioning

confidence: 99%

Research on Detection Method for Tunnel Lining Defects Based on DCAM-YOLOv5 in GPR B-Scan

Chen¹,

Xiong²,

Guo³

2023

RADIOENGINEERING

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This paper presents a detection method of DCAM-YOLOv5 for ground penetrating radar (GPR) to address the difficulty of identifying complex and multi-type defects in tunnel linings. The diversity of tunnel-lining defects and the multiple reflections and scattering caused by water-bearing defects make GPR images quite complex. Although existing methods can identify the position of underground defects from B-scans, their classification accuracy is not high. The DCAM-YOLOv5 adopts YOLOv5 as the baseline model and integrates deformable convolution and convolutional block attention module (CBAM) without adding a large number of parameters to improve the adaptive learning ability for irregular geometric shapes and boundary fuzzy defects. In this study, dielectric constant models of tunnel linings are established based on the electromagnetic simulation software (GPRMAX), including rebar and various structural defects. The simulated and field GPR B-scan images show that the DCAM-YOLOv5 method has better results for detecting different types of defects than other methods, which validates the effectiveness of the proposed detection method.

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Automated Target Detection for Geophysical Applications

Cited by 11 publications

References 24 publications

Machine Learning for the Geosciences: Challenges and Opportunities

Machine Learning for the Geosciences: Challenges and Opportunities

Identification Method of Crop Aphids Based on Bionic Attention

Research on Detection Method for Tunnel Lining Defects Based on DCAM-YOLOv5 in GPR B-Scan

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