Abstract: A large number of short- and medium-span bridges are widely distributed. Traditional structural health monitoring (SHM) methods often require a large number of sensors, leading to high costs and significant challenges in operation and maintenance. To address the need for lightweight health monitoring of these bridges, this paper presents a novel damage identification algorithm based on high-resolution modal local entropy. Only limited sensors are required in this algorithm. The principal component matrix is derived by performing principal component analysis of the displacement responses under moving loads. High-frequency components in the matrix are then filtered using a low-pass filter to obtain high-resolution mode shapes. The concept and derivation of general local entropy (GLE) of high-resolution mode shape are introduced and used as an index to detect structural damage locations. To verify the effectiveness and robustness of the proposed method, numerical simulations and experimental validations have been conducted on a beam bridge model. The results show that the proposed method can successfully identify both the single damage and multiple damages with only limited sensors. This method also eliminates the need for baseline data from undamaged states. Additionally, the results demonstrate its satisfactory performance in terms of noise robustness, making it a promising solution for lightweight structural damage identification in short-and medium-span bridges.