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Aiming at the detection problem of missing small fittings in transmission lines, the acceleration of the inference speed of the missing small fittings algorithm is studied. The multi-task learning method is adopted, and the missing small fittings detection task is connected by a Swin Transformer backbone network and multiple MLP task heads. We propose a new approach of doing multi-task learning as well as multi-task inference, and compare the accuracy against single-task learning in different scenarios. We then verified that new tasks can be seamlessly added to the multi-task learning framework, without any performance degradation. The experimental results show that the speed of proposed multi-task learning is more than 2 times higher than that of single-task learning, and the memory usage is reduced by more than 22%. These experimental results verified the effectiveness of the proposed multi-task learning framework and the flexibility of the task settings.