Research on Recognition and Localization of Cucumber Based on Complex Environment

Abstract

Abstract Lighting variations, leaf occlusion, and fruit overlap make it difficult for mobile picking robots to detect and locate cucumber fruits in complex environments. This paper proposes a novel detection method based on the YOLOv4-tiny-SCE model for cucumbers in a complex environment. It combines the attention mechanism and adaptive spatial feature pyramid method to improve the detection effect of blocked and overlapping cucumbers. Additionally, the method also incorporates a loss function and clustering algorithm to enhance the accuracy and robustness of cucumber detection. On this basis, the 3D spatial coordinate model of cucumber is established using a Realsense depth camera to obtain the target image. To validate the cucumber detection and location method based on the YOLOv4-tiny-SCE model, a comparison experiment between YOLOv4-tiny-SCE and other lightweight models is conducted on the dataset. The results indicate that the YOLOv4-tiny-SCE model achieves an average detection accuracy of 99.7%. The average detection time per image is 0.006s, and there is a 2.5% increase in the F1 score. The average positioning errors of cucumber in X, Y, and Z three-dimensional space are 1.77mm, 2.9mm and 1.8 mm, respectively. This method balances target detection accuracy and model size, which is helpful in realizing the detection and location of cucumbers on low-performance airborne terminals in the future.