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Detecting objects (including all kinds of vehicles, bicycles, and pedestrians) accurately and efficiently is an essential issue in blind-spot information system (BLIS). To meet these requirements, this paper presents an image-based method to detect approaching objects in blind-spot area and proposes a verification method by using the recorded video database from real traffic environment. By taking video frames and converting the images into one dimensional information, the image entropy of the road scene in the near lane are estimated. Thus, by analysis the lane information, an object will be detected and located in a constant time. This idea has been realized and implemented on low-cost DSP platform developed by Automotive Research and Testing Center (ARTC, Taiwan). The accurate rate of this blind-spot detection system (BDS) is 91% and the frame rate is more than 20 frames per sec (fps), in day and night and all weather conditions. The BDS has been applied for general vehicles and heavy truck vehicles nowadays.