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At present, there are two main problems with fruit-and vegetable-picking robots. One is that complex scenes (with backlighting, direct sunlight, overlapping fruit and branches, blocking leaves, etc.) obviously interfere with the detection of fruits and vegetables; the other is that an embedded platform needs a lighter detection method due to performance constraints. To address these problems, a fast tomato detection method based on improved YOLOv3-tiny is proposed. First, we improve the precision of the model by improving the backbone network; second, we use image enhancement to improve the detection ability of the algorithm in complex scenes. Finally, we design several groups of comparative experiments to prove the rationality and feasibility of this method. The experimental results show that the f1-score of the tomato recognition model proposed in this paper is 91.92%, which is 12% higher than that of YOLOv3-tiny; the detection speed on a CPU can reach 25 frames/s, and the inferential speed is 40.35 ms, equivalent to that of YOLOv3-tiny. Through comparative experiments, we can see that the comprehensive performance of our method is better than that of other state-of-the-art methods.INDEX TERMS Real-time object detection, deep learning, picking robot, tomato, embedded device.

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