Detection of typical obstacles in orchards based on deep convolutional neural network

Abstract Precise identification of farmland obstacles is an important environmental-perception task for agricultural vehicles. The orchard environment is a complex and unstructured environment, it is difficult to detect obstacles accurately and effectively. Hence, an improved lightweight object detection method based on YOLOv3 is proposed to identify typical obstacles in orchards, such as humans, cement columns and utility poles. The lightweight MobileNetV2 network was used in the model to reduce the running time when extracting image features, and Gaussian model was introduced to improve the detection effect. By reconstructing the loss function in the original YOLOv3 model, the proposed model can predict the accuracy of the positioning box. Based on the orchard image dataset which contains typical obstacles (humans, cement columns and utility poles) in orchards, comparative experiments were conducted on the accuracy and speed between the proposed model, Faster-RCNN, SSD and the original YOLOv3 model. The test results showed that the proposed model outperforms other models in terms of accuracy and speed. For the total test sets, the F1-score and mean average precision (mAP) were 91.76% and 88.64%, respectively. The prediction time of an image with the size of 416 pixels × 416 pixels on the GPU was 13 ms. Therefore, with a low memory requirement, high recognition accuracy, and fast recognition speed, the method proposed in this study can effectively detect typical obstacles in orchards environment, providing a basis for intelligent orchard robots to avoid obstacles.