The quality of road infrastructure is one of the important factors in supporting the safety and comfort of road users as well as the smooth distribution of transportation. Road maintenance requires periodic monitoring by authorized institutions or agencies. Manual road condition monitoring tends to require considerable time, cost, and manpower, and is also prone to subjectivity. Therefore, a computational system capable of performing this task is needed. Based on this background, this study aims to develop a computer vision-based application for recognizing road conditions. Data consisting of road images with proper annotations (damaged or good) were used to train the YOLOv8 vision model. Our test found that the system accuracy, precision, recall, and F1-score is 0.96, 0.93, 1.00, and 0.96 respectively. The developed application allows users to input road images through a live camera and obtain real-time road condition classification results.
Nathaniel Putra Haryanto, Mohammad Nasucha· Electronic Journal of Educat...· 0 citations
The increasing use of vlog videos on social media creates privacy risks because third-party faces are often unintentionally recorded and distributed without consent. Existing face blurring approaches generally apply uniform anonymization to all detected faces and do not provide an identity-selective mechanism that keeps the content creator visible while blurring other individuals. This study develops PRIVA, a desktop-based selective face blurring application that runs locally without an external AI server. The proposed pipeline integrates YOLOv8n-Face-960 for face detection, MobileFaceNet for face recognition using 512-dimensional embeddings, and Deep SORT for maintaining identity consistency across video frames. Face enrollment is performed through guided multi-pose webcam capture, while video evaluation is conducted on extracted YOLO analysis frames from five real vlog-like test videos. YOLOv8n-Face-960 achieved an overall detection precision of 95.02%, recall of 89.32%, and F1-score of 92.09%. The baseline comparison showed that YOLOv8n-Face-960 achieved a higher mean detection F1-score than MTCNN, while MobileFaceNet provided a smaller and faster recognition model than FaceNet for CPU-based local inference. For correctly detected face instances, PRIVA achieved a system precision of 99.45%, recall of 98.70%, F1-score of 99.08%, and accuracy of 98.50% in determining whether faces should be blurred or kept visible. Processing performance testing showed an average analysis speed of 4.83 FPS, average export speed of 70.05 FPS, and average processing ratio of approximately 2.40 times the original video duration. These results indicate that PRIVA can support practical local identity-selective face blurring for video privacy protection, although detection robustness remains important under low-light, crowded, distant, or partially occluded face conditions.
Muhammad Satrio, Mohammad Nasucha· SinkrOn· 0 citations
