PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

Zhan, Jialei; Hu, Yaowen; Cai, Weiwei; Zhou, Guoxiong; Li, Liujun

doi:10.3390/sym13122260

Cited by 33 publications

(21 citation statements)

References 23 publications

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“…However, the proportion of pixels of smoke and fire targets in aerial forest fire images is too small, and as the number of network layers increases [13], the features and location information of small smoke and fire targets that can be obtained become less, resulting in the network model's poor detection ability of small smoke and fire targets, which is very prone to miss-detection and omission of detection. In order to solve this problem, many scholars have studied small target detection.…”

Section: Related Workmentioning

confidence: 99%

FL-YOLOv7: A Lightweight Small Object Detection Algorithm in Forest Fire Detection

Xiao,

Wan,

Lei

et al. 2023

Forests

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Given the limited computing capabilities of UAV terminal equipment, there is a challenge in balancing the accuracy and computational cost when deploying the target detection model for forest fire detection on the UAV. Additionally, the fire targets photographed by the UAV are small and prone to misdetection and omission during detection. This paper proposes a lightweight, small target detection model, FL-YOLOv7, based on YOLOv7. First, we designed a light module, C3GhostV2, to replace the feature extraction module in YOLOv7. Simultaneously, we used the Ghost module to replace some of the standard convolution layers in the backbone network, accelerating inference speed and reducing model parameters. Secondly, we introduced the Parameter-Free Attention (SimAm) attention mechanism to highlight the features of smoke and fire targets and suppress background interference, improving the model’s representation and generalization performance without increasing network parameters. Finally, we incorporated the Adaptive Spatial Feature Fusion (ASFF) module to address the model’s weak small target detection capability and use the loss function with dynamically adjustable sample weights (WIoU) to weaken the impact of low-quality or complex samples and improve the model’s overall performance. Experimental results show that FL-YOLOv7 reduces the parameter count by 27% compared to the YOLOv7 model while improving 2.9% mAP50small and 24.4 frames per second in FPS, demonstrating the effectiveness and superiority of our model in small target detection, as well as its real-time and reliability in forest fire scenarios.

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Section: Related Workmentioning

confidence: 99%

FL-YOLOv7: A Lightweight Small Object Detection Algorithm in Forest Fire Detection

Xiao,

Wan,

Lei

et al. 2023

Forests

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“…Figure 9 , Figure 10 , Figure 11 and Figure 12 show the comparative detection results of four types of images with the most camera records in actual production, including the current mainstream target detection networks SSD [ 43 ], Faster-RCNN [ 44 ], RetinaNet [ 39 ], YOLOv3 [ 45 ], YOLOv4 [ 35 ], YOLEOv5-L [ 46 ], YOOX-L [ 47 ], and YOLO-BS.…”

Section: Monitoring and Analysis Of Big Coal Blocks In Scraper Conveyormentioning

confidence: 99%

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

Wang

Guo

Zhao

et al. 2022

Sensors

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With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors.

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“…The rapid transmission of big data is now possible thanks to the advancement of wireless information transmission technology. The data transmission time is shortened, and the detection system's reliability [24,25] is improved by continuously optimizing related data transmission technologies.…”

Section: The Framework Of Epse State Awareness Systemmentioning

confidence: 99%

Intelligent Identification of Embedded System Equipment State Based on Internet of Things

Xu¹

2022

Mobile Information Systems

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IoT (Internet of things) technology will be more widely used in the manufacturing industry, which will bring new trends to the development of the manufacturing industry. With the continuous advancement of IoT technology in the power field, the integration of IoT and smart grid has become a new topic. In the process of monitoring and troubleshooting the whole EPSE (Embedded Power System Equipment), it is very important to conduct a comprehensive analysis and routine testing on specific equipment and systems, so that the power system can work stably. In this paper, the knowledge sharing and transfer model of EPSE based on IoT is analyzed, and an intelligent identification method of EPSE state based on the GA-C_HMM (Genetic Algorithm-Coupled Hidden Markov Model) algorithm is proposed. After the GA parameters are optimized, the state model library is constructed by fitting each state data of the equipment to C_HMM, and the state of the equipment is determined by calculating the maximum probability value of the signal to be diagnosed. The experimental results show that when the recognition time is 12 hours, the recognition accuracy rate of the existing recognition method is 69.7%, and that of the recognition method in this paper is 98.3%, which shows that the recognition accuracy rate of the recognition method adopted in this study is higher than that of the existing methods and the recognition ability is stronger.

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PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

Cited by 33 publications

References 23 publications

FL-YOLOv7: A Lightweight Small Object Detection Algorithm in Forest Fire Detection

FL-YOLOv7: A Lightweight Small Object Detection Algorithm in Forest Fire Detection

A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS

Intelligent Identification of Embedded System Equipment State Based on Internet of Things

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