2021
DOI: 10.3390/sym13122260
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PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

Abstract: The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small for… Show more

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Cited by 33 publications
(21 citation statements)
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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%
“…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%
“…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%
“…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%