An inspection algorithm based on cascade classifiers is presented for detecting small moving foreign substances with low Signal and Noise Ratio (SNR) and low contrast in sequential images. The algorithm obtains three features of absolute difference, local difference contrast and neighborhood correlation from the sequential images of an ampoule. Each feature corresponds to a classifier, and small foreign substances are inspected by using three-layer cascade classifiers. The first layer corresponds to a traditional frame differencing method, which is used to remove the background and detect the large moving foreign substances. The next two layers are used to inspect small foreign substances and remove the noises generated by optical flow and the stain of bottle. Experiment results show that compared with the traditional frame differencing method, this algorithm has higher detection precision and higher anti-interference ability in inspecting small substances with the interference of a complex background, and the detection rate of small foreign substance is 99.3%. This algorithm can meet the requirement of real-time detection of ampoules for medicine production.