The peak point location is susceptible to many factors of the fluorescence immunochromatographic quantitative image, which can cause the problem of low substance quantification accuracy. To address this issue, a cascaded convolutional neural network (CNN) algorithm for fusion target detection is proposed. The improved AlexNet is utilized in the first-level cascade algorithm to detect and extract the regions containing the quality control (C) peak and test (T) peak in the fluorescence immunochromatographic quantitative image. The extracted image area is sent to the second-level cascaded convolutional neural network to locate C peak and T peak quickly. Then, the location results are taken as the input of the third-level cascaded convolutional neural network. The fine-tune the location results of the C peak and T peak can be realized from the previous layer. Finally, the accurate location information of the C peak and T peak is achieved. Experimental results show that the proposed cascaded convolutional neural network algorithm can locate the peak points of fluorescence immunochromatography images with the accuracy of more than 96% , and the location accuracy of peak points is enhanced.