With the continuous development of autonomous driving technology, realizing high-precision road obstacle detection is crucial to ensure traffic safety and driving experience. However, traditional obstacle detection methods often perform poorly in complex driving scenarios, such as obstacle movement and occlusion. To cope with this problem, this study proposes a road obstacle detection method based on a two-stream convolutional neural network model, aiming to overcome the limitations of traditional methods in capturing spatiotemporal features and handling complex situations. Our research approach is based on the following innovations: First, we introduce a dual-stream convolutional neural network structure, where one stream is used to extract spatial features from the contour information of the obstacle frames, and the other stream extracts temporal features from the temporal stream information. This dual-stream structure can fully capture the appearance and dynamic information of the obstacles, thus improving detection accuracy. Second, we design a feature fusion module to fuse the two features to obtain richer obstacle features. In addition, we propose a new loss function, i.e. clustering loss, for better optimizing the model training process, reducing intra-class variation, and increasing inter-class differences, thus improving the generalization performance of the model. In the experimental section, we conducted extensive experimental analysis using Citycapes and BDD100K datasets. The experimental results show that our model achieves significant performance improvement compared to both the traditional convolutional neural network method and the YOLOv5 method in a variety of scenarios such as obstacle stationary, obstacle moving, and obstacle with occlusion. Specifically, our method improves the recognition rate up to 4.8% to 14.5% on the Citycapes dataset and 6.5% to 12.8% on the BDD100K dataset, respectively, under different scenarios. In addition, our model also exhibits more advantages on small datasets, showing higher generalization ability and robustness.

As mobile applications evolve rapidly, their fast iterative update nature leads to an increase in software defects. Just-In-Time Software Defect Prediction (JIT-SDP) offers immediate feedback on code changes. For new applications without historical data, researchers have proposed Cross-Project JIT-SDP (CP JIT-SDP). Existing CP JIT-SDP approaches are designed for offline scenarios where target data is available in advance. However, target data in real-world applications usually arrives online in a streaming manner, making online CP JIT-SDP face cross-project distribution differences and target project data concept drift challenges in online scenarios. These challenges often co-exist during application development, and their interactions cause model performance to degrade. To address these issues, we propose an online CP JIT-SDP framework called COTL. Specifically, COTL consists of two stages: offline and online. In the offline stage, the cross-domain structure preserving projection algorithm is used to reduce the cross-project distribution differences. In the online stage, target data arrives sequentially over time. By reducing the differences in marginal and conditional distributions between offline and online data for target project, concept drift is mitigated and classifier weights are updated online. Experimental results on 15 mobile application benchmark datasets show that COTL outperforms 13 benchmark methods on four performance metrics.