Oil Tank Detection With Improved EfficientDet Model

Xu, Su; Zhang, Haowei; He, Xiping; Cao, Xiaoli; Hu, Jian

doi:10.1109/lgrs.2022.3183350

Cited by 10 publications

(6 citation statements)

References 34 publications

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“…To verify the effect of our algorithm in bubble detection, the comparative experiments between improved YOLOv8n and the other mainstream algorithms are conducted under the same experimental environment, the results of which are presented in Table 3 below. In the Table 3, we can see that our algorithm has higher accuracy compared to SSD, Faster R-CNN, YOLOv5s, YOLOv7-tiny, EfficientDet-D1 [39][40] and SpineNet-49 [41], with an increase of 7.2%, 4.1%, 5%, 3.3%,1.6% and 0.9% in mAP, respectively. Compared to the improved YOLOv8n algorithm we proposed, the SpineNet model has a much lower detection efficiency, and the model size is nearly ten times larger than ours.…”

Section: Comparative Experimentsmentioning

confidence: 99%

Research on Bubble Detection Based on Improved YOLOv8n

Chen,

Zeng

2024

IEEE Access

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Bubble detection has important applications in industries such as tightness testing, fluid measurement, healthcare, and chemical engineering. Due to the fact that bubbles belong to small targets with characteristics such as reflection and shadows, they have fewer available features and are highly susceptible to environmental interference, which makes it difficult for detection models to accurately locate and recognize the bubbles. Aiming at these problems, an improved YOLOv8n for bubble detection is proposed. Firstly, the deformable convolution network (DCN) is used in the Backbone module to take place of the original C2f module, which enables the model to have stronger feature extraction and adaptive generalization capabilities for small targets with different deformations. Then the global attention mechanism (GAM) is introduced into Neck network to locally enhance the channels or regions of interest, which is beneficial for capturing the important features of targets, especially small targets like bubbles. Finally, the loss function is improved so that boundary box regression and target detection become more accurate. Experiments are conducted on the bubble dataset, and the results indicate the mean average precision (mAP) of the improved algorithm reaches 97.4%, increasing by 2.2% compared to the original YOLOv8n.This shows the proposed method has better comprehensive performance on bubble detection.

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Section: Comparative Experimentsmentioning

confidence: 99%

Research on Bubble Detection Based on Improved YOLOv8n

Chen,

Zeng

2024

IEEE Access

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“…Figure 1 An overview of proposed EfficientDet Object Detection Working Pipeline using EfficientNet backbone, BiSkFPN bottleneck and MFL head. [16] While performing AMODMV, biggest challenge is the noisy images (limited visibility) because of the impurities of water, wrong placement angles of image sensing kits, and interpreting the results from the object detection model. In addition, The marine environment is often cluttered with a variety of objects, such as rocks, plants, and debris, which can make it difficult to distinguish between objects of interest and background clutter [2,3].…”

Section: Low Level Featuresmentioning

confidence: 99%

“…A new method that uses AM to dampen the effect of noise (caused by pollution, clouds, and climate) in remote sensing images. This work [16] also modifies pooling in every layer such that it can capture tiny class specific pixels and hence uses exhaustive feature space. This approach increases computational complexity but helps to achieve higher accuracy.…”

Section: 1-mynet: Improved Efficientdet Using Attention Mechanism (Am...mentioning

confidence: 99%

Underwater Object Detection Based on Improved EfficientDet

Jia

et al. 2022

Remote Sensing

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Intelligent detection of marine organism plays an important part in the marine economy, and it is significant to detect marine organisms quickly and accurately in a complex marine environment for the intelligence of marine equipment. The existing object detection models do not work well underwater. This paper improves the structure of EfficientDet detector and proposes the EfficientDet-Revised (EDR), which is a new marine organism object detection model. Specifically, the MBConvBlock is reconstructed by adding the Channel Shuffle module to enable the exchange of information between the channels of the feature layer. The fully connected layer of the attention module is removed and convolution is used to cut down the amount of network parameters. The Enhanced Feature Extraction module is constructed for multi-scale feature fusion to enhance the feature extraction ability of the network to different objects. The results of experiments demonstrate that the mean average precision (mAP) of the proposed method reaches 91.67% and 92.81% on the URPC dataset and the Kaggle dataset, respectively, which is better than other object detection models. At the same time, the processing speed reaches 37.5 frame per second (FPS) on the URPC dataset, which can meet the real-time requirements. It can provide a useful reference for underwater robots to perform tasks such as intelligent grasping.

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“…Oil tanks are essential energy storage devices and critical infrastructures which are widely used in petroleum, natural gas, petrochemical industries and transportation (Xu et al, 2022;Yu et al, 2021). Rapid and accurate detection of oil tanks is substantial in terms of disaster management, risk evaluation and monitoring (Ok and Başeski, 2015;Xu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…They obtained better accuracy values compared to state-of-the-art segmentation models such as U-Net, SegNet and PSPNet. Xu et al (2022) present an improved version of EfficientDet architecture using 3-D deformable convolution, attention mechanism and focal loss function. They have achieved better accuracy results compared to various models which are generally region proposal-based architectures.…”

Section: Introductionmentioning

confidence: 99%

An Assessment of YOLO Architectures for Oil Tank Detection from SPOT Imagery

Bakırman

2023

International Journal of Environment and Geoinformatics

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Since it can be used to manage and estimate oil reserves, the inventory of oil tanks is essential for both the economy and the military applications. Considering oil tanks contain valuable materials required for transportation and industrial production, they are a significant type of target. Oil tank detection techniques have several uses, including monitoring disasters, preventing oil leaks, designing cities, and assessing damage. Huge amount of satellite imagery has recently been available and it is used in both the military and civil applications. The new spaceborne sensors' higher resolution enables the detection of targeted objects. Therefore, remote sensing instruments provide ideal tools for oil tank detection task. Conventional approaches for oil tank detection from high resolution remote sensing imagery generally relies on geometric shape, structure, contract differences and color information of the boundary or hand-crafted features. However, these methods come along with vulnerabilities and hence it can be challenging to obtain accurate detection in the presence of a number of disturbance elements, particularly a wide range of colours, size variations, and the shadows that view angle and illumination create. Therefore, deep learning-based methods can provide a big advantage for solution of this task. In this regard, this study employs four YOLO models namely YOLOv5, YOLOX, YOLOv6 and YOLOv7 for oil tank detection from high-resolution optical imagery. Our results show that YOLOv7 and YOLOv5 architectures provide more accurate detections with mean average precision values of 68.11% and 69.69%, respectively. The experiments and visual inspections reveal efficiency, generalization and transferability of these models.

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Oil Tank Detection With Improved EfficientDet Model

Cited by 10 publications

References 34 publications

Research on Bubble Detection Based on Improved YOLOv8n

Research on Bubble Detection Based on Improved YOLOv8n

Underwater Object Detection Based on Improved EfficientDet

An Assessment of YOLO Architectures for Oil Tank Detection from SPOT Imagery

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