Deep Convolutional Neural Network untuk Mendeteksi Retak pada Permukaan Beton yang Memiliki Void

Convolutional neural network berbasis encoder-decoder telah dirancang dan dilatih menggunakan dataset eksternal untuk mendeteksi retak pada permukaan beton yang relatif sederhana. Namun, pada kenyataannya permukaan beton memiliki banyak fitur seperti void pada permukaan yang disebabkan oleh udara yang terperangkap saat proses pencampuran beton. Oleh karena itu, pada penelitian ini kemampuan convolutional neural network akan diteliti lebih lanjut untuk mendeteksi retak pada permukaan beton yang memiliki void. Tujuan pertama penelitian ini adalah menguji model yang dilatih dengan dataset eksternal pada permukaan beton ber-void. Jika model tidak berhasil membedakan void dengan retak, maka tujuan kedua penelitian ini adalah menyusun dataset pelatihan internal baru yang secara khusus membedakan void dengan retak, yang kemudian akan ditambahkan pada dataset eksternal untuk diinvestigasi performanya. Penelitian ini menggunakan arsitektur U-Net dan arsitektur DeepLabV3+ sebagai encoder-decoder untuk mengoperasikan semantic image segmentation. Model encoder-decoder yang dilatih dengan dataset eksternal tidak berhasil membedakan void dengan retak saat pengujian. Maka, dataset internal yang terdiri dari gambar beton ber-void dibentuk dan digabungkan dengan dataset eksternal. Dengan penambahan dataset internal yang baru, hasil pengujian menunjukkan bahwa model berhasil membedakan void dengan retak pada permukaan beton. U-Net mencapai nilai F1 sebesar 85,92%, sedangkan DeepLabV3+ mencapai nilai F1 sebesar 84,09%

Machine Vision and Deep Learning Based Rubber Gasket Defect Detection

This study develops an automated optical inspection system for silicone rubber gaskets using traditional rule-based and deep learning detection techniques. The specific object of interest is a 5 mm × 10 mm × 5 mm  mobile device power supply connector gasket that provides protection against foreign body inclusion and water ingression. The proposed system can detect a total of five characteristic defects introduced during the mold-based manufacture process, which range from 10-100 μm. The deep learning detection strategies in this system employ convolutional neural networks (CNN) developed using the TensorFlow open-source library. Through both high dynamic range image capture and image generation techniques, accuracies of 100% and 97% are achieved for notch and residual glue defect predictions, respectively

Crack Detection on Concrete Surfaces Using Deep Encoder-Decoder Convolutional Neural Network: A Comparison Study Between U-Net and DeepLabV3+

Maintenance of infrastructures is a crucial activity to ensure safety using crack detection methods on concrete structures. However, most practice of crack detection is carried out manually, which is unsafe, highly subjective, and time-consuming. Therefore, a more accurate and efficient system needs to be implemented using artificial intelligence. Convolutional neural network (CNN), a subset of artificial intelligence, is used to detect cracks on concrete surfaces through semantic image segmentation. The purpose of this research is to compare the effectiveness of cutting-edge encoder-decoder architectures in detecting cracks on concrete surfaces using U-Net and DeepLabV3+ architectures with potential in biomedical, and sparse multiscale image segmentations, respectively. Neural networks were trained using cloud computing with a high-performance Graphics Processing Unit NVIDIA Tesla V100 and 27.4 GB of RAM. This study used internal and external data. Internal data consisted of simple cracks and were used as the training and validation data. Meanwhile, external data consisted of more complex cracks, which were used for further testing. Both architectures were compared based on four evaluation metrics in terms of accuracy, F1, precision, and recall. U-Net achieved segmentation accuracy = 96.57%, F1 = 87.55%, precision = 88.15%, and recall = 88.94%, while DeepLabV3+ achieved segmentation accuracy = 96.47%, F1 = 85.29%, precision = 92.07%, and recall = 81.84%. Experiment results (internal and external data) indicated that both architectures were accurate and effective in segmenting cracks. Additionally, U-Net and DeepLabV3+ exceeded the performance of previously tested architecture, namely FCN

Concrete Surface Crack Detection with Convolutional-based Deep Learning Models

Effective crack detection is pivotal for the structural health monitoring and inspection of buildings. This task presents a formidable challenge to computer vision techniques due to the inherently subtle nature of cracks, which often exhibit low-level features that can be easily confounded with background textures, foreign objects, or irregularities in construction. Furthermore, the presence of issues like non-uniform lighting and construction irregularities poses significant hurdles for autonomous crack detection during building inspection and monitoring. Convolutional neural networks (CNNs) have emerged as a promising framework for crack detection, offering high levels of accuracy and precision. Additionally, the ability to adapt pre-trained networks through transfer learning provides a valuable tool for users, eliminating the need for an in-depth understanding of algorithm intricacies. Nevertheless, it is imperative to acknowledge the limitations and considerations when deploying CNNs, particularly in contexts where the outcomes carry immense significance, such as crack detection in buildings. In this paper, our approach to surface crack detection involves the utilization of various deep-learning models. Specifically, we employ fine-tuning techniques on pre-trained deep learning architectures: VGG19, ResNet50, Inception V3, and EfficientNetV2. These models are chosen for their established performance and versatility in image analysis tasks. We compare deep learning models using precision, recall, and F1 scores.Comment: 11 pages, 3 figures, Journal pape