首页|基于CenterNet的自然场景下苹果快速识别方法

基于CenterNet的自然场景下苹果快速识别方法

扫码查看
目的 构建一种高效的苹果目标识别方法,提升苹果采摘机器人在果园自然场景中的目标识别准确率和效率.方法 通过采用CenterNet神经网络为检测框架,同时融入了分组卷积和深度可分离卷积的理念,设计了一种基于瓶颈结构堆叠策略的轻量级特征提取网络Light-Weight Net.结果 设计了 一种适配于苹果采摘机器人视觉系统的识别算法,实现了在果园自然场景中高精度和高效率的苹果目标识别.结论 该模型在测试集上实现了 96.60%的目标识别准确率(以平均精度衡量),通过与YOLOv3和Efficient-D0模型在相同测试集进行对比,试验结果平均精度分别提高了 6.30%和5.17%,单幅图像平均识别时间分别快了 0.014 s和0.05 s.
CenterNet-based approach for fast apple recognition method in natural scenes
Purposes—To design a fast apple target recognition method for improving the target recognition accuracy and recognition efficiency of an apple picking robot in a natural scene in an or-chard.Methods—The CenterNet neural network is used as the detection framework,and a Light-Weight Net lightweight feature extraction network is proposed by drawing on the ideas of grouped convolution and depth-separable convolution.Results—The recognition algorithm adapted to the visual system of apple picking robots has been designed which achieves high-precision and efficient apple tar-get recognition in natural orchard scenes.Conclusions—The model recognized an AP value of 96.60%under the test set,and by comparing with YOLOv3 and Efficient-D0 model in the same test set,the experimental results showed that the AP value was improved by 6.30%and 5.17%,and the average recognition time of a single image was faster by 0.014 s and 0.05 s.

picking robotnatural scenesapple recognitionCenterNet

樊攀、孙瑾、周桥、陈瞾宇

展开 >

宝鸡文理学院计算机学院,陕西宝鸡 721016

采摘机器人 自然场景 苹果识别 CenterNet

陕西省教育厅青年创新团队科研计划项目

23JP004

2024

宝鸡文理学院学报(自然科学版)
宝鸡文理学院

宝鸡文理学院学报(自然科学版)

影响因子:0.356
ISSN:1007-1261
年,卷(期):2024.44(3)