Automatic detection of illegal parking on highways through surveillance video is a crucial task for modern traffic management systems because it directly affects traffic flow，public safety，and operational efficiency. However，detecting parking violations in highway environments presents substantial challenges because of complex traffic dynamics，varying lighting conditions，and broad surveillance coverage. These challenges include frequent vehicle occlusions in dense traffic that lead to identity confusion and incorrect tracking，substantial perspective distortion that causes slow-moving vehicles to be mistakenly identified as parked，and degraded detection performance under low-illumination conditions，where vehicle features become blurred and are prone to false detection because of environmental lighting effects.

To address these problems，this study proposes a novel highway illegal parking detection algorithm that integrates dual-attention feature fusion and spatiotemporal behavior analysis. The approach introduces a dual-attention fusion module to enhance multiscale feature representations extracted from YOLOv8，which are used as appearance descriptors in a vehicle tracking framework on the basis of BotSORT-ReID. This framework directly leverages YOLOv8’s multiscale feature outputs and enhances them with attention mechanisms in spatial and channel dimensions. The spatial attention module emphasizes critical regions related to vehicle targets，suppressing background noise，whereas the channel attention mechanism selectively emphasizes discriminative feature channels relevant to object identity. These enhancements substantially improve the robustness and accuracy of vehicle tracking，especially in scenarios involving occlusion，background clutter，or varying viewing angles. The second component of the algorithm focuses on violation judgment through spatiotemporal feature accumulation. Specifically，it uses an adaptive interframe displacement estimation method on the basis of exponentially weighted moving averages to identify low-speed or stationary vehicles over a sequence of frames. A normalized displacement model is employed to correct perspective distortions across different regions of the image，ensuring that movement estimates are reliable even when vehicle scale varies with the distance from the camera. This method smooths short-term fluctuations in object position and helps avoid misclassification caused by detection box jitter or gradual deceleration. A secondary validation mechanism is proposed to address the complexity of nighttime detection，especially in environments with poor visibility or interference from artificial lights. This process involves analyzing the temporal characteristics of brightness changes to identify flashing hazard lights，which are a strong indicator of stationary vehicles in emergency situations. Time-series analysis is conducted on grayscale intensity values extracted from target bounding boxes across frames. Preprocessing steps，including Savitzky-Golay filtering，detrending，and temporal gradient computation，are applied to isolate periodic brightness variations. Subsequently，Welch’s method is used for power spectral density estimation，and dominant frequencies are extracted and compared with known hazard light flashing frequency ranges. Only when these frequencies match expected patterns is the object confirmed to be illegally parked，thereby reducing false positives from nonvehicle light sources or reflections.

Given the absence of publicly available datasets for highway parking violation detection，three dedicated highway datasets are constructed for object detection，vehicle tracking，and parking violation recognition. The datasets consist of 8 532 labeled images for detection，34 600 frames for tracking，and 77 annotated video sequences （average over 1 min，covering 98 illegal parking vehicles） for violation recognition. Moreover，they cover diverse conditions，including daytime，nighttime，tunnels，and adverse weather （e.g.，rain and fog），thereby ensuring comprehensive evaluation across complex highway scenarios. Experimental results on the constructed datasets show that the proposed algorithm consistently outperforms several benchmark methods in terms of precision，recall，and F1 score. Notably，dual-attention feature extraction improves identity consistency in vehicle tracking，thereby reducing ID switches and increasing tracking accuracy. The adaptive displacement method demonstrates strong resistance to jitter and perspective artifacts，and the hazard light detection module substantially reduces nighttime false positives and improves robustness under difficult lighting. Ablation studies further confirm the contribution of each component. For instance，removing the dual-attention module results in reduced tracking quality and an increase in identity confusion. Without the adaptive displacement method，the system becomes highly vulnerable to misclassification because of minor motion or camera instability. Meanwhile，excluding the temporal signal-based hazard light detection leads to a rise in false alarms during nighttime evaluations. The full system achieves a balanced trade-off between high precision and high recall，demonstrating its effectiveness in real-world applications.

Overall，the proposed algorithm presents a unified，efficient，accurate solution for illegal parking detection on highways. By combining deep learning-based object detection，attention-guided feature fusion，motion pattern recognition，and signal-based validation，this algorithm addresses the key limitations of current methods and achieves robust performance across varying highway surveillance conditions. The integration of lightweight attention modules into the YOLOv8 backbone and the design of spatiotemporal behavioral analysis allow for real-time processing while maintaining high accuracy. The system is thus well-suited for deployment in intelligent transportation systems and smart city infrastructure where reliability and adaptability are paramount. The methodology also provides a scalable framework for extending similar detection strategies to other traffic violations or abnormal behaviors in complex road environments.