This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,an...This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.展开更多
New ultra-lightweight sludge-red mud ceramics(ULS-RMC) were prepared by red mud(RM),clay and dried sewage sludge(DSS).The properties and mechanism of RM in the preparation of ULS-RMC were discussed.The chemical compon...New ultra-lightweight sludge-red mud ceramics(ULS-RMC) were prepared by red mud(RM),clay and dried sewage sludge(DSS).The properties and mechanism of RM in the preparation of ULS-RMC were discussed.The chemical components,thermal properties and mineral phases of RM were determined by energy dispersive X-ray(EDX),differential scanning calorimetry/thermal gravimetric analysis(DSC/TGA) and X-ray diffraction(XRD),respectively.Constant dosage of DSS to clay and different amounts of RM were utilized in the preparation of ULS-RMC.Physical properties test(bulk density,grain density,water absorption and expansion ratio),XRD and scanning electron microscopy(SEM) were employed to characterize the ULS-RMC.The results show that RM exhibits high hydroscopic property and good water-retention property,and bloating property and fluxing property of RM are caused by abound of gaseous components and flux,respectively.The two chemical properties are utilized to discuss the mineral phases and microstructures differences between ULSC and ULS-RMC.展开更多
基金Supported by the Fundamental Research Funds for the Central Universities(2024300443)the Natural Science Foundation of Jiangsu Province(BK20241224).
文摘This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.
基金Project(2010013111005) supported by the Ph.D Programs Foundation of Ministry of Education of China
文摘New ultra-lightweight sludge-red mud ceramics(ULS-RMC) were prepared by red mud(RM),clay and dried sewage sludge(DSS).The properties and mechanism of RM in the preparation of ULS-RMC were discussed.The chemical components,thermal properties and mineral phases of RM were determined by energy dispersive X-ray(EDX),differential scanning calorimetry/thermal gravimetric analysis(DSC/TGA) and X-ray diffraction(XRD),respectively.Constant dosage of DSS to clay and different amounts of RM were utilized in the preparation of ULS-RMC.Physical properties test(bulk density,grain density,water absorption and expansion ratio),XRD and scanning electron microscopy(SEM) were employed to characterize the ULS-RMC.The results show that RM exhibits high hydroscopic property and good water-retention property,and bloating property and fluxing property of RM are caused by abound of gaseous components and flux,respectively.The two chemical properties are utilized to discuss the mineral phases and microstructures differences between ULSC and ULS-RMC.
