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In order to improve the recognition efficiency and environmental adaptability of the apple picking robot, so that it can quickly and accurately recognize multiple apple targets in dense scenes, a rapid recognition method for multiple apple targets in dense scenes was proposed. The method drew on the idea of “point is the target”, and realized the rapid identification of apple targets by predicting the center point of apple and the width and height of apple. By improving the CenterNet network, the Tiny Hourglass-24 lightweight backbone network was designed, and the residual module was optimized to improve the target recognition speed. The test results showed that the average recognition accuracy of this method on the test set in non-dense scenes (images taken in close-range scenes) was 98.90%, and F1 was 96.39%. In the dense scene (images taken in the remote scene), the recognition average precision (AP) of the test set was 93.63%, the F1 was 92.91%, and the average recognition time of a single image was 0.069s. By comparing with the recognition effect of YOLO v3 and CornerNet-Lite network under the two types of test sets, the AP of this method was increased by 4.13 percentage points and 29.03 percentage points respectively on the dense scene test set. The average image recognition time was 0.04s faster than that of YOLO v3 and 0.646s faster than that of CornerNet-Lite. This method did not need to use anchor box and non-maximum suppression post-processing, and can provide technical support for the apple picking robot to quickly and accurately identify multiple apple targets in dense scenes.