To improve the recognition rate and reduce the false-recognition rate in real-time detection of flame in video surveillance, a fast flame recognition algorithm based on multi-feature logarithm regression model was proposed. Firstly, the image was segmented according to the chromaticity of the flame, and the Candidate Fire Region (CFR) was obtained by subtracting the moving target image with reference image. Secondly the features of the CRF such as area change rate, circularity, number of sharp corners and centroid displacement were extracted to establish the logarithmic regression model. Then, a total of 300 images including flame and non-flame images, which were got from National Institute of Standards and Technology (NIST), Computer Vision laboratory of Inha University (ICV), Fire detection based on computer Vision (VisiFire) and the experimental library consisting of the candle and paper combustion were used to parametric learning. Finally, 8 video clips including 11071 images were used to validate the proposed algorithm. The experimental results show that the True Positive Rate (TPR) and True Negative Rate (TNR) of the proposed algorithm are 93% and 98% respectively. The average time of identification is 0.058 s/frame. Because of its fast identification and high recognition rate, the proposed algorithm can be applied in embedded real-time flame image recognition.

In order to denoise the Pulsed Eddy Current (PEC) signal from industrial field, a real-time hardware and software system based on heterogeneous dual-core OMAP3530 was designed. First, how to select a wavelet basis was analyzed in the real-time process. The Matlab denoising model based on DB4 wavelet threshold method including Penalty method, B-M method and default method was established, and the Root of Mean Square Error (RMSE) and Signal-to-Noise Ratio (SNR) of the denoising model were given. With reference to the OMAP3530 hardware platform, the system software design of data sharing based on DSPLINK of heterogeneous dual-core was discussed. A real-time filtering algorithm based on fixing length step window was promoted to denoise the PEC signal. The simulation and experimental results indicate that the real-time denoising system has the features of high SNR, strong real-time and high throughput rate to satisfy the requirements of auto-testing on the casting production line.