School of Electronics Engineering, Vellore Institute of Technology, Chennai Campus, Chennai 600127, India.
Department of ECE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam 603110, India.
Sensors (Basel). 2023 Mar 8;23(6):2954. doi: 10.3390/s23062954.
This research article is aimed at improving the efficiency of a computer vision system that uses image processing for detecting cracks. Images are prone to noise when captured using drones or under various lighting conditions. To analyze this, the images were gathered under various conditions. To address the noise issue and to classify the cracks based on the severity level, a novel technique is proposed using a pixel-intensity resemblance measurement (PIRM) rule. Using PIRM, the noisy images and noiseless images were classified. Then, the noise was filtered using a median filter. The cracks were detected using VGG-16, ResNet-50 and InceptionResNet-V2 models. Once the crack was detected, the images were then segregated using a crack risk-analysis algorithm. Based on the severity level of the crack, an alert can be given to the authorized person to take the necessary action to avoid major accidents. The proposed technique achieved a 6% improvement without PIRM and a 10% improvement with the PIRM rule for the VGG-16 model. Similarly, it showed 3 and 10% for ResNet-50, 2 and 3% for Inception ResNet and a 9 and 10% increment for the Xception model. When the images were corrupted from a single noise alone, 95.6% accuracy was achieved using the ResNet-50 model for Gaussian noise, 99.65% accuracy was achieved through Inception ResNet-v2 for Poisson noise, and 99.95% accuracy was achieved by the Xception model for speckle noise.
这篇研究文章旨在提高使用图像处理检测裂缝的计算机视觉系统的效率。使用无人机或在各种光照条件下拍摄的图像容易受到噪声的影响。为了分析这一点，在各种条件下收集了图像。为了解决噪声问题并根据严重程度对裂缝进行分类，提出了一种使用像素强度相似性测量 (PIRM) 规则的新方法。使用 PIRM 对有噪声和无噪声的图像进行分类。然后，使用中值滤波器过滤噪声。使用 VGG-16、ResNet-50 和 InceptionResNet-V2 模型检测裂缝。一旦检测到裂缝，就使用裂缝风险分析算法对图像进行分割。根据裂缝的严重程度，可以向授权人员发出警报，以便采取必要的措施避免重大事故。对于 VGG-16 模型，没有使用 PIRM 时，该技术提高了 6%，使用 PIRM 规则时提高了 10%。同样，对于 ResNet-50 模型，它分别提高了 3%和 10%，对于 Inception ResNet 提高了 2%和 3%，对于 Xception 模型提高了 9%和 10%。当图像仅受到一种噪声的污染时，使用 ResNet-50 模型对高斯噪声的准确率达到 95.6%，使用 Inception ResNet-v2 对泊松噪声的准确率达到 99.65%，使用 Xception 模型对斑点噪声的准确率达到 99.95%。
