Abstract: In recent years, the pollution from diesel vehicle exhaust emissions in China has become increasingly severe. In order to improve the atmospheric environment, it is necessary to monitor diesel vehicles emitting black smoke. However, in urban traffic road scenarios, the detection of black smoke vehicles is often difficult to determine through rear-view videos due to factors such as mutual obstruction between vehicles. Additionally, the severe lack of relevant data greatly limits the effectiveness of the data. To address the above problems, this paper proposes a black smoke diesel vehicle re-identification model under the cross-camera scene. By introducing the IBN module to construct a feature extraction network, the adaptability of the network model to changes in the appearance of diesel vehicle images is enhanced. A loss function based on the Hausdorff distance metric learning is designed to measure the feature differences, increasing inter-class distance and reducing the impact of occluded samples during the optimization process. Then, benchmark datasets for diesel vehicle repositioning across multiple scenarios are constructed, and the proposed method is experimented on this dataset. The experimental results show that the proposed method achieves a relative accuracy of 83.79%, demonstrating high accuracy.

Key words: transversal cameras, black smoke vehicle re-identification, metric learning