Wildfires are natural disasters that cause extensive human casualties and economic losses worldwide. Rapid detection in the early stages is essential to minimize damage; however, conventional supervised learning-based object detection methods face limitations due to the difficulty of acquiring sufficient labeled data for rare wildfire events and their high implementation costs. This study proposes an unsupervised wildfire-detection framework that leverages the characteristics of fixed closed-circuit television (CCTV) environments. The core of the proposed method is a Learnable Concentration Filtering Module that quantifies the spatial distribution differences between wildfire smoke and atmospheric clouds. To extract robust feature representations, we employ ConvNeXt, a modernized convolutional architecture that integrates Vision Transformer design principles to achieve superior performance. Multi-scale features are extracted using a this backbone, and each feature map is divided into fixed 14 × 14 patches. Spatial correlations between patches are learned through local-window self-attention. From the decoder-based reconstruction error map, the concentration ratio defined as the ratio of local maximum to mean values are computed at multiple scales, and concentrated anomaly patterns are selectively extracted through learnable thresholds. Experimental results on four regions of the HPWREN dataset achieved an average area under the receiver operating characteristic curve (AUROC) of 0.801, average precision (AP) of 0.689, and maximum F1-score (F1-max) of 0.769, demonstrating improved performance over other state-of-the-art anomaly detection models. The proposed method achieved a fast inference time of 10.53 ms, confirming its real-time processing capability. This study presents a practical approach for developing wildfire-detection systems that are robust to environmental variations and do not require labeled data.

License plate recognition is a computer vision technology that plays a crucial role in intelligent transportation systems and vehicle management. However, in real-world road environments, recognition accuracy significantly decreases due to distortions caused by various viewing angles. In particular, existing systems exhibit severe performance degradation when processing license plate images captured at steep angles. This paper proposes a new approach to solve the license plate recognition problem in such unconstrained environments. To accurately recognize text on distorted license plates, it is crucial to precisely locate the four corners of the plate and correct the distortion. For this purpose, the proposed system incorporates vehicle and license plate detection based on YOLOv8 and integrates a Corner Enhancement Module (CEM) utilizing a Deformable Convolutional Network (DCN) into the model’s neck to ensure robust feature extraction against geometric transformations. Additionally, the system significantly improves corner detection accuracy through parallel ensemble processing of three license plate images: the original and two aspect ratio-adjusted versions (2:1 and 1.5:1). Furthermore, we verified the system’s versatility in real road environments by implementing a real-time license plate recognition system using Raspberry Pi 4 and a camera module.