Fire situation detection method for unmanned fire-fighting vessel based on coordinate attention structure-based deep learning network

Wang, Shengzheng; Zhang, Yunfan; Hsieh, Tsung-Hsuan; Liu, Wei; Yin, Fang‐Fang; Liu, Bin

doi:10.1016/j.oceaneng.2022.113208

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…4 demonstrates the effect of image augmentation, with the expanded dataset containing a greater variety of sugarcane states and morphologies, aiming to consider the operational environment in actual sugarcane fields as much as possible and to include a greater number of sugarcane internodes in the images. In this study, Imgaug [23] was employed as the code tool for data augmentation. Imgaug is an open-source Python package suitable for various augmentation techniques, providing simplicity, convenience, and efficiency.…”

Section: Image Augmentation In the Datasetmentioning

confidence: 99%

Design and Experimental Evaluation of an Intelligent Sugarcane Stem Node Recognition System based on Enhanced YOLOv5s

Dai,

Luo,

Shi

et al. 2024

Preprint

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The rapid and accurate identification of sugarcane internodes is of great significance for tasks such as field operations and precision management in the sugarcane industry, and it is also a fundamental task for the intelligence of the sugarcane industry. However, in complex field environments, traditional image processing techniques have low accuracy, efficiency, and are mainly limited to server-side processing. Meanwhile, the sugarcane industry requires a large amount of manual involvement, leading to high labor costs. In response to the aforementioned issues, this paper employed YOLOv5s as the original model algorithm, incorporated the K-means clustering algorithm, and added the CBAM attention module and VarifocalNet mechanism to the algorithm. The improved model is referred to as YOLOv5s-KCV. We implemented the YOLOv5s-KCV algorithm on Jetson TX2 edge computing devices with a well-configured runtime environment, completing the design and development of a real-time sugarcane internode identification system. Through ablation experiments, comparative experiments of various mainstream visual recognition network models, and performance experiments conducted in the field, the effectiveness of the proposed improvement method and the developed real-time sugarcane internode identification system were verified. The experimental results demonstrate that the improvement method of YOLOv5s-KCV is effective, with an algorithm recognition accuracy of 89.89%, a recall rate of 89.95%, and an mAP value of 92.16%, which respectively increased by 6.66%, 5.92%, and 7.44% compared to YOLOv5s. The system underwent performance testing in various weather conditions and at different times in the field, achieving a minimum recognition accuracy of sugarcane internodes of 93.5%. Therefore, the developed system in this paper can achieve real-time and accurate identification of sugarcane internodes in field environments, providing new insights for related work in sugarcane field industries.

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Section: Image Augmentation In the Datasetmentioning

confidence: 99%

Design and Experimental Evaluation of an Intelligent Sugarcane Stem Node Recognition System based on Enhanced YOLOv5s

Dai,

Luo,

Shi

et al. 2024

Preprint

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“…(2) Adaptive spatial feature fusion (ASFF) module To capture multi-scale feature information of ship emissions, an Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced into the modified Tiny-BiFPN structure, as described in the preceding section [28]. The ASFF mechanism is designed to enhance multiscale feature fusion in situations where the network depth is limited.…”

Section: Improvement Strategy For Multi-feature Fusion Mechanismmentioning

confidence: 99%

Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature Fusion

Wang,

Han,

et al. 2023

JMSE

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To enhance the real-time detection accuracy of ship exhaust plumes and further quantify the degree of darkness, this study proposes a multi-feature fusion approach that combines the YOLOv5s-CMBI algorithm for ship exhaust plume detection with the Ringerman Blackness-based grading method. Firstly, diverse datasets are integrated and a subset of the data is subjected to standard optical model aerosolization to form a dataset for ship exhaust plume detection. Subsequently, building upon the YOLOv5s architecture, the CBAM convolutional attention mechanism is incorporated to augment the network’s focus on ship exhaust plume regions while suppressing irrelevant information. Simultaneously, inspired by the BiFPN structure with weighted bidirectional feature pyramids, a lightweight network named Tiny-BiFPN is devised to enable multi-path feature fusion. The Adaptive Spatial Feature Fusion (ASFF) mechanism is introduced to counteract the impact of feature scale disparities. The EIoU_Loss is employed as the localization loss function to enhance both regression accuracy and convergence speed of the model. Lastly, leveraging the k-means clustering algorithm, color information is mined through histogram analysis to determine clustering centers. The Mahalanobis distance is used to compute sample similarity, and the Ringerman Blackness-based method is employed to categorize darkness levels. Ship exhaust plume grades are estimated by computing a weighted average grayscale ratio between the effective exhaust plume region and the background region. Experimental results reveal that the proposed algorithm achieves improvements of approximately 3.8% in detection accuracy, 5.7% in recall rate, and 4.6% in mean average precision (mAP0.5) compared to the original model. The accuracy of ship exhaust plume darkness grading attains 92.1%. The methodology presented in this study holds significant implications for the establishment and application of future ship exhaust plume monitoring mechanisms.

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“…In this paper, we propose the YOLOv5 algorithm based on attention mechanism, the main work is to piggyback the attention mechanism CA(Coordinate Attention) on the lightweight neural network YOLOv5s, which further improves the target detection accuracy while maintaining the target detection speed and lightweight; meanwhile, we use the K-means++ clustering algorithm [12] to filter the anchor frame on the tank dataset, which not only improves the training efficiency also improves the accuracy of detection results; CA algorithm [13][14] is introduced to enhance the small target and occlusion target feature extraction capability and improve the generalization ability and robustness of the model. The experiments prove that YOLOv5s-CA achieves better results on the tank dataset, and the overall performance is better than YOLOv5s, which achieves the expected goal.…”

Section: Introductionmentioning

confidence: 99%

UAV target detection algorithm based on improved YOLOv5s

Zhang,

Wang,

Chen

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Real-time UAV monitoring is an important means of battlefield reconnaissance, and machine interpretation of UAV images has become the main form of image interpretation, so the merit of the algorithm becomes an important factor limiting UAV reconnaissance. To address the problems of insufficient graphics card arithmetic power, low detection accuracy and difficult deployment of algorithm models at the embedded end of UAVs, this paper proposes an improved lightweight target detection algorithm based on YOLOv5s, adding K-means++ algorithm and CA attention mechanism module to the original algorithm, and training the improved YOLOv5s-CA network using tank dataset, and the simulation results show that: the improved YOLOv5s-CA has an mAP value of 97.50%, an F1 value of 0.96, and an FPS value of 74.8, which can be deployed on UAVs for real-time detection.

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Fire situation detection method for unmanned fire-fighting vessel based on coordinate attention structure-based deep learning network

Cited by 13 publications

References 37 publications

Design and Experimental Evaluation of an Intelligent Sugarcane Stem Node Recognition System based on Enhanced YOLOv5s

Design and Experimental Evaluation of an Intelligent Sugarcane Stem Node Recognition System based on Enhanced YOLOv5s

Research on Black Smoke Detection and Class Evaluation Method for Ships Based on YOLOv5s-CMBI Multi-Feature Fusion

UAV target detection algorithm based on improved YOLOv5s

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