To address the hazy weather image degradation problem, we propose a single image dehazing method based on a physical model and the brightness components of the image by using a multi-scale retinex with color restorati...
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To address the hazy weather image degradation problem, we propose a single image dehazing method based on a physical model and the brightness components of the image by using a multi-scale retinex with color restoration algorithm. The overall dehazing process involves three components, including the atmospheric light value calculation, transmission map estimation, and recovery of the hazy image scene radiance. Our contribution is that we propose a novel algorithm to dehaze a single image by calculating the atmospheric light value and computing the transmission map while considering the dynamic range of the image. Experimental results show that our algorithm can effectively improve the image quality degraded by foggy weather and retain sufficient image details.
During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be *** this study,the multi-scale Retinex with color restoration(MSR...
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During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be *** this study,the multi-scale Retinex with color restoration(msrcr)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light *** enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent *** that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold ***,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture *** users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this *** with the manifold ranking algorithm,the K-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,***,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared *** proposed method accurately recognized the green apples under complex illumination conditions and growth ***,it provided effective references for intelligent growth monitoring and yield estimation of fruits.
The elbow portion of a pipeline plays a critical role in pipeline systems, and defects can lead to potential gas and oil leakage accidents. Magnetic flux leakage (MFL) detection is an efficient method for identifying ...
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The elbow portion of a pipeline plays a critical role in pipeline systems, and defects can lead to potential gas and oil leakage accidents. Magnetic flux leakage (MFL) detection is an efficient method for identifying pipeline defects. To address the issue of image distortion in defect MFL signals, the accuracy of defect recognition must be improved. This paper proposes an intelligent identification method for MFL defects in small-diameter pipe elbows based on a deep learning target detection algorithm. The msrcr algorithm is enhanced using bilateral filtering and gamma correction to improve the image features of defect MFL signals. Additionally, the YOLOv5 network is augmented with CBAM, Soft-NMS and Focal-EIOU loss to enhance the feature extraction capabilities. The results show that the proposed improved msrcr algorithm effectively solves the elbow MFL defect distortion problem. The improved YOLOv5 network achieves average defect recognition accuracies of 86.61% and 94.27% on the original and enhanced datasets, respectively. After experimental verification, the network effectively improves the recognition accuracy of MFL defects in small-diameter pipe elbows, and provides useful technical support for the intelligent detection and safety evaluation of elbows.
With the development of construction industry, the traditional manual inspection has been gradually eliminated due to many shortcomings, such as low efficiency, time-consuming and labor-intensive. Meanwhile, the curre...
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With the development of construction industry, the traditional manual inspection has been gradually eliminated due to many shortcomings, such as low efficiency, time-consuming and labor-intensive. Meanwhile, the current helmet detection model on the market does not consider the interference of complex weather, which greatly affects the detection performance. A low-cost helmet detection scheme is proposed in this paper, which can be used in various complex weather environments such as heavy rain, fog and snow. Firstly, the monitoring video of the construction site is sliced as the helmet wearing detection data set, and improved on the basis of Yolo v5s model to make it meet the requirements of helmet detection. Secondly, data augmentation and oversampling are adopted to improve the accuracy for small targets. Finally, K-means++ clustering algorithm is utilized to change the dimension of anchor box for better detection performance, and msrcr algorithm is used to filter complex weather conditions. Compared to the original Yolo v5s, the mean average precision of the proposed scheme achieves 94.27% under complex weather conditions. For images with a size of 300*300, the detection speed can reach 63 frames per second. Therefore, the scheme can realize a high-precision, real-time and low-cost helmet detection system which can be used in a complex weather environments effectively.
In order to overcome the low recall rate, peak signal-to-noise ratio and correct recognition rate of spatial pattern in traditional spatial pattern recognition methods, a landscape spatial pattern recognition method b...
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In order to overcome the low recall rate, peak signal-to-noise ratio and correct recognition rate of spatial pattern in traditional spatial pattern recognition methods, a landscape spatial pattern recognition method based on multi-source remote sensing images was proposed. First, we obtain multi-source remote sensing images of landscape architecture, use ORB algorithm to extract multi-source remote sensing image feature points, and fuse multi-source remote sensing images. Then, msrcr algorithm is used to enhance the fused image, LOG edge detection operator is used to obtain the image edge, and MCR model is used to determine the landscape patch characteristics. Finally, the spatial pattern recognition model of landscape architecture is built, and the spatial pattern recognition results are obtained. The experimental results show that the maximum recall rate of this method is 97%, the maximum peak signal to noise ratio of image is 59.3 dB, and the correct recognition rate varies from 97% to 99%.
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