Bias-variance tradeoff in practice (CNN)
Normally, the training loss is lower than the validation one. This does not indicate any overfitting. Indeed, it is even suspicious when you training loss is higher than the validation loss. From other hand, worsening of the validation accuracy while improving on the train set definitely tells you that you overfits. 

Generally speaking, overfitting means bad generalization, memorization of the training set rather than learning a generic concepts behind the data. Besides the metrics during the training you can find it out by trying your model on external datasets from a similar but not the same domain/distribution. Very poor accuracy will indicate the overfitting that might be hidden by a validation set very similar to the training set.