A method for identifying and locating fruits to be picked based on depth target detection

Abstract

The invention discloses a method for identifying and locating fruit to be picked based on depth target detection, comprising the following steps: image acquisition, image annotation. data Set Preparation. Feature extraction includes using depth convolution neural network CNN to extract the fruit features in the image first; VGG16 is used as a convolution neural network for feature extraction. Model training, For all the training samples, the feature maps on each convolution layer are extracted firstly, and the prediction boxes are generated for the extracted feature maps, the categories of thetargets in the prediction boxes are calculated, the distance between the prediction boxes and the real annotation boxes is calculated, and the joint category loss and the prediction box offset loss are used as the target loss function of the training during the training process. A training is completed based on the complete one-time calculation process described above, and the training ends whenthe number of training times reaches a predetermined threshold or the loss is less than the predetermined threshold.

Description

technical field [0001] The invention relates to a method for identifying and locating fruits to be picked, in particular to a method for identifying and locating fruits to be picked based on depth target detection. Background technique [0002] Due to the complexity of the natural environment where fruit picking operations are located, fruit picking still relies on manpower, and the labor required for fruit picking accounts for more than half of the labor input in the entire production process. With the continuous decline of the agricultural employment population and the continuous increase of labor costs in my country, automatic fruit picking is of great significance to solve the labor shortage in the fruit industry, ensure timely fruit picking, and improve picking quality. Therefore, it is imminent to study the automatic picking technology of fruits. [0003] Quickly and reliably finding the fruit target and determining the location of the fruit to be picked in the natura...

AI Technical Summary

Problems solved by technology

The idea of ​​the existing algorithm is mainly to manually design and extract features for the texture, color and shape of the fruit to be picked. When the occlusion and lighting conditions change with the scene under natural conditions, the corresponding features of the fruit to be picked also Therefore, the robustness of the fruit feature extraction algorithm based on artificial feature design needs to be improved; under natural production conditions, the acquired fruit images usually have complex backgrounds such as occlusion or uneven illumination. Fruit recognition rate and positioning accuracy under growing conditions need to be improved

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