In the natural environment, the detection and recognition process of Punna navel orange fruit using machine vision systems is affected by many factors, such as complex background, uneven light illumination, occlusions of branches and leaves and large variations in fruit size. To solve these problems of low accuracy in fruit detection and poor robustness of the detection algorithm in the field conditions, a new object detection algorithm, named Retinanet_G2S, was proposed in this paper based on the modified Retinanet network. The images of Punna navel orange were collected with Microsoft Kinect V2 in the uncontrolled environment. Firstly, a new Res2Net-GF network was designed to replace the section of feature extraction in the original Retinanet, which can potentially improve the learning ability of target features of the trunk network. Secondly, a multi-scale cross-regional feature fusion grids network was designed to replace the feature pyramid network module in the original Retinanet, which could enhance the ability of feature information fusion among different scales of the feature pyramid. Finally, the original border regression localization method in Retinanet network was optimized based on the accurate boundary box regression algorithm. The study results showed that, compared with the original Retinanet network, Retinanet_G2S improved mAP, mAP50, mAP75, mAP_S, mAP_M and mAP_L by 3.8%, 1.7%, 5.8%, 2.4%, 2.1% and 5.5%, respectively. Moreover, compared with 7 types of classic object detection models, including SSD, YOLOv3, CenterNet, CornerNet, FCOS, Faster-RCNN and Retinanet, the average increase in mAP of Retinanet_G2S was 9.11%. Overall, Retinanet_G2S showed a promising optimization effect, particularly for the detection of small targets and overlapping fruits.

在自然环境中，机器视觉系统对普纳脐橙果实的检测和识别过程受背景复杂、光照不均、枝叶遮挡、果实大小变化大等因素影响。针对水果检测精度低和检测算法在野外条件下鲁棒性差的问题，本文基于改进的Retinanet网络提出了一种新的目标检测算法Retinanet_G2S。普纳脐橙的图像是在不受控制的环境中使用 Microsoft Kinect V2 采集的。首先，设计了一个新的Res2Net-GF网络来代替原来Retinanet中的特征提取部分，这可以潜在地提高主干网络的目标特征的学习能力。其次，设计了多尺度跨区域特征融合网格网络来替代原Retinanet中的特征金字塔网络模块，增强了不同尺度特征金字塔之间的特征信息融合能力。最后，在精确边界框回归算法的基础上，对Retinanet网络中原有的边界回归定位方法进行了优化。研究结果表明，与原Retinanet网络相比，Retinanet_G2S提高了mAP、mAP50、mAP75、mAP_S、mAP_M 和 mAP_L 分别提高 3.8%、1.7%、5.8%、2.4%、2.1% 和 5.5 ％， 分别。此外，与SSD、YOLOv3、CenterNet、CornerNet、FCOS、Faster-RCNN、Retinanet等7种经典目标检测模型相比，Retinanet_G2S的mAP平均提升了9.11%。总体而言，Retinanet_G2S表现出了良好的优化效果，特别是对于小目标和重叠水果的检测。