In the field of industrial visual monitoring, PTZ cameras need to monitor the leakage of different easy leakage points. The backgrounds of the monitoring images change greatly and the characteristics of leakage targets are small, which will lead to large intra-class differences and small inter-class differences in the dataset samples. These problems affect the ability of the detection network to learn different class features of the leakage images and restrict the leakage detection performance. To solve the above problems, this paper proposes a pipeline leakage video detection method based on loss joint supervision in multiple scenes. Firstly, C3D network is used to simultaneously extract the spatial and temporal characteristics of the pipeline leakage video. In addition, the random forest classifier is used to avoid the tedious gradient calculation operation in the training process. Finally, by adding triple loss and center loss to jointly supervise model training, we measure the similarity within classes and the difference between classes to improve the decision-making ability of the leakage detection classifier. The experiment result shows that our method has a detection accuracy of 96.72% for pipeline leakage video in industrial environment and the training time is shortened by 18.75%.