Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model

Yang, Zhaoxu; Shao, Yifan; Wei, Ye; Li, Jun

doi:10.3390/app14062413

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Instance-level imbalance means that the pixel area of small-and medium-sized objects in the MS COCO only accounts for 1%, and augmentation is solved using the copy-paste method. However, these two thoughts do not fundamentally solve the problem, which has strong limitations [27].…”

Section: Related Workmentioning

confidence: 99%

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

Fu,

Tao,

Deng

et al. 2024

Applied Sciences

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Image detection technology is of paramount importance across various fields. This significance is not only seen in general images with everyday scenes but also holds substantial research value in the field of remote sensing. Remote sensing images involve capturing images from aircraft or satellites. These images typically feature diverse scenes, large image formats, and varying imaging heights, thus leading to numerous small-sized targets in the captured images. Accurately identifying these small targets, which may occupy only a few pixels, is a challenging and active research area. Current methods mainly fall into two categories: enhancing small target features by improving resolution and increasing the number of small targets to bolster training datasets. However, these approaches often fail to address the core distinguishing features of small targets in the original images, thus resulting in suboptimal performance in fine-grained classification tasks. To address this situation, we propose a new network structure DDU (Downsample Difference Upsample), which is based on differential and resolution changing methods in the Neck layer of deep learning networks to enhance the recognition features of small targets, thus further improving the feature richness of recognition and effectively solving the problem of low accuracy in small target object recognition. At the same time, in order to take into account the recognition effect of targets of other sizes in the image, a new attention mechanism called PNOC (protecting the number of channels) is proposed, which integrates small target features and universal object features without losing the number of channels, thereby increasing the accuracy of recognition. And experimental verification was conducted on the PASCAL-VOC dataset. At the same time, it was applied to the testing of the fine-grained MAR20 dataset and found that the performance was better than other classic algorithms. At the same time, because the proposed framework belongs to a one-stage detection method, it has good engineering applicability and scalability, and universality in scientific research applications are good. Through comparative experiments, it was found that our algorithm improved the performance of the mAP by 0.7% compared to the original YOLOv8 algorithm.

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Section: Related Workmentioning

confidence: 99%

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

Fu,

Tao,

Deng

et al. 2024

Applied Sciences

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Visual fire detection using deep learning: A survey

Cheng,

Chen,

Wang

et al. 2024

Neurocomputing

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Research on Defect Detection for Overhead Transmission Lines Based on the ABG-YOLOv8n Model

Yu,

Lv,

Chen

et al. 2024

Energies

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In the field of smart grid monitoring, real-time defect detection for overhead transmission lines is crucial for ensuring the safety and stability of power systems. This paper proposes a defect detection model for overhead transmission lines based on an improved YOLOv8n model, named ABG-YOLOv8n. The model incorporates four key improvements: Lightweight convolutional neural networks and spatial–channel reconstructed convolutional modules are integrated into the backbone network and feature fusion network, respectively. A bidirectional feature pyramid network is employed to achieve multi-scale feature fusion, and the ASFF mechanism is used to enhance the sensitivity of YOLOv8n’s detection head. Finally, comprehensive comparative experiments were conducted with multiple models to validate the effectiveness of the proposed method based on the obtained prediction curves and various performance metrics. The validation results indicate that the proposed ABG-YOLOv8n model achieves a 4.5% improvement in mean average precision compared to the original YOLOv8n model, with corresponding increases of 3.6% in accuracy and 2.0% in recall. Additionally, the ABG-YOLOv8n model demonstrates superior detection performance compared to other enhanced YOLO models.

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Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model

Cited by 4 publications

References 36 publications

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

Image Detection Network Based on Enhanced Small Target Recognition Details and Its Application in Fine Granularity

Visual fire detection using deep learning: A survey

Research on Defect Detection for Overhead Transmission Lines Based on the ABG-YOLOv8n Model

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