This paper considers distributed detection over large scale connected networks with arbitrary topology. Contrasting to the canonical parallel fusion network where a single node has access to the outputs from all other sensors, each node can only exchange one-bit information with its direct neighbors in the present setting. Our approach adopts a novel consensus reaching algorithm using asymmetric bounded quantizers that allow controllable consensus error. Under the Neyman-Pearson criterion, we show that, with each sensor employing an identical one-bit quantizer for local information exchange, this approach achieves the optimal error exponent of centralized detection provided that the algorithm converges. Simulations show that the algorithm converges when the network is large enough.