False cracks, such as split joints and scratches, have macroscopic geometry that is similar to real cracks, which can influence the crack detection efficiency for a concrete bridge. To solve this problem, a crack detection algorithm based on the mesoscale geometric features of cracks is proposed. Through the mesoscale analysis of concrete crack formation and propagation mechanisms, it is found that a concrete crack propagates at the interface between aggregates and mortar and usually has a meandering path, whereas a false crack’s path is usually smooth or even straight. Thus the path smoothness of a crack candidate is chosen as the detection basis. The algorithm extracts a crack candidate with conventional methods, and also its skeleton for representing the path. In addition, the feature parameters are designed to quantify the path smoothness. Moreover, a back propagation neural network (BPNN) and a support vector machine (SVM) for the classification of crack candidates are trained using the proposed feature parameters as the input. Experimental results show that the classification rate of the BPNN trained by new features is 91.7%, which is better than the BPNNs trained by conventional features. The classification rate of the SVM is 93.3%, which is more suitable for engineering in small size samples.