For any kind of neural network to be powerful, it needs to contain non-linearity. The result of the convolution operation is hence passed through the non-linear activation function. ReLU activation is used in general to achieve non-linearity (and to combat the vanishing gradient problem with sigmoid activation). So, the values in the final feature maps are not actually the sums, but the relu function applied to them.