A new detection algorithm for alien intrusion on highway

Guo, Junmei; Lou, Haitong; Chen, Haonan; Liu, Haiying; Bi, Lingyun; Duan, Xuehu

doi:10.1038/s41598-023-37686-w

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…The YOLO v8 model structure is shown in Figure 5, which integrates the advantages of various YOLO series, enabling YOLO v8 to achieve higher accuracy in various object detection tasks, establishing it as the state-of-the-art (SOTA) in the current YOLO series [43,52]. Although YOLO v8 has robust object detection capabilities, it cannot directly input multisource remote sensing data.…”

Section: Amf Gd Yolo V8 Modelmentioning

confidence: 99%

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Zhong,

Zhang,

Liu

et al. 2024

Forests

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Automatic and accurate individual tree species identification is essential for the realization of smart forestry. Although existing studies have used unmanned aerial vehicle (UAV) remote sensing data for individual tree species identification, the effects of different spatial resolutions and combining multi-source remote sensing data for automatic individual tree species identification using deep learning methods still require further exploration, especially in complex forest conditions. Therefore, this study proposed an improved YOLOv8 model for individual tree species identification using multisource remote sensing data under complex forest stand conditions. Firstly, the RGB and LiDAR data of natural coniferous and broad-leaved mixed forests under complex conditions in Northeast China were acquired via a UAV. Then, different spatial resolutions, scales, and band combinations of multisource remote sensing data were explored, based on the YOLOv8 model for tree species identification. Subsequently, the Attention Multi-level Fusion (AMF) Gather-and-Distribute (GD) YOLOv8 model was proposed, according to the characteristics of the multisource remote sensing forest data, in which the two branches of the AMF Net backbone were able to extract and fuse features from multisource remote sensing data sources separately. Meanwhile, the GD mechanism was introduced into the neck of the model, in order to fully utilize the extracted features of the main trunk and complete the identification of eight individual tree species in the study area. The results showed that the YOLOv8x model based on RGB images combined with current mainstream object detection algorithms achieved the highest mAP of 75.3%. When the spatial resolution was within 8 cm, the accuracy of individual tree species identification exhibited only a slight variation. However, the accuracy decreased significantly with the decrease of spatial resolution when the resolution was greater than 15 cm. The identification results of different YOLOv8 scales showed that x, l, and m scales could exhibit higher accuracy compared with other scales. The DGB and PCA-D band combinations were superior to other band combinations for individual tree identification, with mAP of 75.5% and 76.2%, respectively. The proposed AMF GD YOLOv8 model had a more significant improvement in tree species identification accuracy than a single remote sensing sources and band combinations data, with a mAP of 81.0%. The study results clarified the impact of spatial resolution on individual tree species identification and demonstrated the excellent performance of the proposed AMF GD YOLOv8 model in individual tree species identification, which provides a new solution and technical reference for forestry resource investigation combined multisource remote sensing data.

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Section: Amf Gd Yolo V8 Modelmentioning

confidence: 99%

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Zhong,

Zhang,

Liu

et al. 2024

Forests

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“…YOLO V8 is the newest addition to the popular YOLO group of object detection models, which Ultralytics developed. This state-ofthe-art model builds on the strengths of old versions of YOLO and provides a faster and more efficient detection system (Guo et al, 2023). One of the critical features of YOLO V8 is its new backbone network, which improves its accuracy and speed.…”

Section: Yolo V8 Model Descriptionmentioning

confidence: 99%

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

Khan,

Raza,

Abbas

et al. 2024

Front. Built Environ.

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Autonomous vehicles can transform the transportation sector by offering a safer and more effective means of travel. However, the success of self-driving cars depends on their ability to navigate complex road conditions, including the detection of potholes. Potholes pose a substantial risk to vehicles and passengers, leading to potential damage and safety hazards, making their detection a critical task for autonomous driving. In this work, we propose a robust and efficient solution for pothole detection using the “you look only once (YOLO) algorithm of version 8, the newest deep learning object detection algorithm.” Our proposed system employs a deep learning methodology to identify real-time potholes, enabling autonomous vehicles to avoid potential hazards and minimise accident risk. We assess the effectiveness of our system using publicly available datasets and show that it outperforms existing state-of-the-art approaches in terms of accuracy and efficiency. Additionally, we investigate different data augmentation methods to enhance the detection capabilities of our proposed system. Our results demonstrate that YOLO V8-based pothole detection is a promising solution for autonomous driving and can significantly improve the safety and reliability of self-driving vehicles on the road. The results of our study are also compared with the results of YOLO V5.

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“…The concept of a sea highway represents a comprehensive and practical approach to addressing disparities in economic development, as noted by Guo et al (2023) and Sameer et al (2023). It is anticipated that the sea highway will effectively mitigate income disparities between western and eastern regions by targeting a reduction in the high costs associated with internal trade.…”

Section: Introductionmentioning

confidence: 99%

The Impact of the T-3 Sea Toll Program on Price Disparity

Firmantoko,

Simanjuntak,

Bashori

et al. 2024

meteor stip marunda

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The sea toll program is to facilitate and streamline the transportation of goods through sea transportation for logistics distribution needs in archipelagic areas that do not have good transportation. The sea toll program can provide benefits not only for price stability of basic commodities but also benefits to the regional economy. The study aims to analyze the impact of sea toll benefits on price disparities. Specifically, the research was conducted in the Tarempa area of the Sea Toll Route -Route T-3. The research used quantitative method with data analysis using the Wilcoxon method. The results of data analysis show that the sea toll program for the T3 route route shows that there is a price disparity in the need for basic goods and essential materials. Data analysis from the Asymp Sig. (2-tailed) value> 0.05, namely 0.06, so H0 is accepted, where H0 there is no difference between the price before the sea toll and after the sea toll. Research conclusions based on the price of staples according to the results of the Wilcoxon test there is no difference between before the sea highway and after the sea highway.

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A new detection algorithm for alien intrusion on highway

Cited by 10 publications

References 23 publications

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

The Impact of the T-3 Sea Toll Program on Price Disparity

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