Research On Small Target Pine Cone Detection Method Based On Deep Learning

Korean pine is the most important economic tree species in Northeast China.Pinecone picking is an important issue in the management of Korean pine forests.At present,this work is mainly done manually.Manual picking not only requires workers to have high professional skills,but also has the disadvantages of high cost and high risk.To develop an effective detection method to detect and locate the pinecone,and integrate it into the picking robot,which can provide technical support for the mechanized harvesting of pinecone.However,due to the influence of factors such as small target and complex detection environment,there are some difficulties in pinecone detection,and few relevant studies in academic circles.The existing methods of pinecone detection are also based on traditional methods,which have the disadvantages of slow detection speed and low detection accuracy.With the development of computer vision,the use of deep learning technology to solve the problem of object detection has become the mainstream direction of current research,and its detection speed and detection accuracy are greatly beyond the traditional detection methods.Based on the analysis of the research status at home and abroad,this paper focusing on the problems insufficient training data of detection model,low detection accuracy of small target pinecone object,and poor detection speed of algorithm,studies a small target pinecone detection method based on deep learning,and systematically integrates it with the designed pinecone picking robot.Specific research achievements and innovations include:A small target pinecone image data enhancement model based on generation adversarial network(PC-GAN)is proposed to solve the problem of difficult pinecone image data acquisition and insufficient training data set of detection model.In this model,the size of the generated image and the dimension of the potential noise vector are adjusted to make the data distribution of the generated image continuous,so as to enhance the authenticity of image region features.In the model network design,the automatic encoder structure is used to improve the texture definition of the generated image,and the long jump connection is used to enhance the shallow information flow between networks,so that the small target pinecone details are completed.The loss function based on Wasserstein distance is designed to solve the problem of model collapse and training instability in the generation adversarial network.Furthermore,the spatial constraint,structural similarity algorithm and discriminator reconstruction loss are combined to further improve the image data generation performance of the model.This paper proposes an improved YOLOv4 small target pinecone detection algorithm based on multi-scale features and model compression technology,which improves the detection accuracy and detection speed for small target pinecone objects.Aiming at the feature extraction problem of small target pinecones,use dense connection and cross-stage local network to enhance the transfer of semantic information of small target pinecones in the network,and then multi-scale feature extraction was achieved through hierarchical structure.Aiming at the problem of small target pinecone feature fusion,a multi-scale feature fusion method based on scale equilibrium pyramid principle was designed.The 3D fusion was used to realize the mutual utilization of different size feature maps,so as to enhance the effect of feature fusion.Aiming at the problem of poor algorithm detection speed,a model compression method based on channel pruning was designed.This method uses different pruning methods for jumping connections at different positions,achieves the balance between compression rate and detection accuracy through multiple iterations of pruning scheme,and further improves the compression effect of the model by combining with batch normalized folding technology.A pinecone detection system based on the machine vision of the picking robot was constructed,and the integration of the pinecone detection algorithm with the picking machinery was realized.Firstly,the technical requirements of pinecone picking task were analyzed to determine the selection scheme of mobile platform,vision module and picking robot arm.Secondly,the mechanical structure of the picking robot arm and end-effector was designed according to the actual working environment in the Korean pine forest,and the physical model of the prototype pinecone picking robot was constructed.Finally,the corresponding pinecone detection system was designed according to the task function requirements of the pinecone picking robot,and the pinecone detection algorithm was integrated with the picking robot.The experimental results show that the IS score and FID score of small target pinecone images generated by PC-GAN are 7.78 and 19.01,respectively,which have good image quality.After image fusion,the generated image can effectively expand the pinecone image data set,thus improve the accuracy and robustness of the detection algorithm.The AP value of the improved YOLOv4 detection algorithm is increased by 6%,the calculation cost and storage demand of the model are reduced by 48.2% and 38.8%,respectively,which significantly improves the detection accuracy and detection speed of small target pinecone objects.The pinecone detection algorithm of small target is integrated with the pinecone picking robot,which can effectively detect the pinecone target,and complete the pinecone picking task under the simulated environment.The research results of this paper provide technical support for the development of pinecone picking equipment and has reference significance.