{"title":"Nighttime airport runway FOD intrusive detection through frequency-domain interference of spatially aggregated dynamic feature","authors":"Guangchen Chen, Yinhui Zhang, Zifen He, Ying Huang","doi":"10.1016/j.eswa.2025.128719","DOIUrl":null,"url":null,"abstract":"<div><div>Accidental intrusion of foreign object debris (FOD) on the airport runway often causes fatal safety hazards in aviation transportation during take-off and landing especially at night as the degraded imaging quality. To address the false and missed detections in low-light images by current advanced methods, the Frequency-Domain Interference Network of spatially aggregated dynamic feature (FDI-Net) is proposed to improve the recognition accuracy of threatening FOD at nighttime. Firstly, to deal with the challenges posed by degraded low-quality imaging at night, we propose the Frequency-Domain Adaptive Tuning (FDAT) spatial pooling module, which utilizes fast Fourier transformation to construct frequency-domain features from the row and column pixels of FOD images. Subsequently, generating the wave function signal through the superposition of row and column components, and adaptively tuning the frequency and phase spectra using dynamically learnable weights within the network structure. This process effectively suppresses redundant information while enhancing the grayscale and texture feature space. Secondly, the Dynamic Granular Aggregated Interference (DGAI) module is developed to transform the FOD spatial features into amplitude and phase representations, enabling the extraction of fine-grained feature information through dynamic depth fusion. This module then aggregates the fine-grained features using the interference effects of sine and cosine waves to enhance positive FOD target information and suppress negative interference. Finally, the detection model is deployed on an embedded edge computing platform to develop a mobile nighttime airport runway foreign object debris detection system. Experimental results demonstrate that the proposed model effectively detects ten categories of small and medium-scale FOD targets, achieving optimal accuracy results of 97.9 %, 89.8 %, and 77.6 % on the mAP50, mAP75, and mAP50-95 metrics, respectively. In addition, our method achieves the accuracy of 94.5 %, 89.9 %, 85.2 %, 76.2 %, 92.4 %, 92.2 %, 95.5 %, 74.4 %, 97.5 %, and 99.5 % on ten categories, respectively. Consequently, the intelligent detection system holds significant potential for preventing accidents caused by FOD in the aviation transportation safety field.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"294 ","pages":"Article 128719"},"PeriodicalIF":7.5000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417425023371","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Accidental intrusion of foreign object debris (FOD) on the airport runway often causes fatal safety hazards in aviation transportation during take-off and landing especially at night as the degraded imaging quality. To address the false and missed detections in low-light images by current advanced methods, the Frequency-Domain Interference Network of spatially aggregated dynamic feature (FDI-Net) is proposed to improve the recognition accuracy of threatening FOD at nighttime. Firstly, to deal with the challenges posed by degraded low-quality imaging at night, we propose the Frequency-Domain Adaptive Tuning (FDAT) spatial pooling module, which utilizes fast Fourier transformation to construct frequency-domain features from the row and column pixels of FOD images. Subsequently, generating the wave function signal through the superposition of row and column components, and adaptively tuning the frequency and phase spectra using dynamically learnable weights within the network structure. This process effectively suppresses redundant information while enhancing the grayscale and texture feature space. Secondly, the Dynamic Granular Aggregated Interference (DGAI) module is developed to transform the FOD spatial features into amplitude and phase representations, enabling the extraction of fine-grained feature information through dynamic depth fusion. This module then aggregates the fine-grained features using the interference effects of sine and cosine waves to enhance positive FOD target information and suppress negative interference. Finally, the detection model is deployed on an embedded edge computing platform to develop a mobile nighttime airport runway foreign object debris detection system. Experimental results demonstrate that the proposed model effectively detects ten categories of small and medium-scale FOD targets, achieving optimal accuracy results of 97.9 %, 89.8 %, and 77.6 % on the mAP50, mAP75, and mAP50-95 metrics, respectively. In addition, our method achieves the accuracy of 94.5 %, 89.9 %, 85.2 %, 76.2 %, 92.4 %, 92.2 %, 95.5 %, 74.4 %, 97.5 %, and 99.5 % on ten categories, respectively. Consequently, the intelligent detection system holds significant potential for preventing accidents caused by FOD in the aviation transportation safety field.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.