识别表面类型的能力对于室内和室外移动机器人都至关重要。了解表面类型可以帮助室内移动机器人更安全地移动并相应地调整其移动。然而,识别表面特征是具有挑战性的,因为相似的平面可能看起来大不相同;例如,地毯有各种类型和颜色。为了解决基于视觉的表面分类中的这种固有不确定性,这项研究首先产生了一个新的,由2,081张表面图像组成的独特数据集(地毯,瓷砖,和木材)在不同的室内环境中捕获。其次,预先训练的最先进的深度学习模型,即InceptionV3、VGG16、VGG19、ResNet50、Xception、利用InceptionResNetV2和MobileNetV2来识别表面类型。此外,提出了一种轻量级的MobileNetV2修改模型用于表面分类。所提出的模型的总参数比原始MobileNetV2模型少大约四倍,将训练的模型权重的大小从42MB减少到11MB。因此,该模型可用于计算能力有限的机器人系统和嵌入式系统。最后,几个优化器,如SGD,RMSProp,亚当,Adadelta,Adamax,Adagrad,还有Nadam,用于区分最有效的网络。实验结果表明,所提出的模型优于所有其他应用的方法和现有的方法在文献中达到99.52%的准确率和平均得分99.66%的精度。召回,和F1得分。除此之外,在由办公室等各种室内环境组成的11种场景中,在移动机器人上实时测试了所提出的轻量级模型,走廊,和家园,导致99.25%的准确度。最后,根据模型加载时间和处理时间对每个模型进行评估.所提出的模型比其他模型需要更少的负载和处理时间。
The ability to recognize the surface type is crucial for both indoor and outdoor mobile robots. Knowing the surface type can help indoor mobile robots move more safely and adjust their movement accordingly. However, recognizing surface characteristics is challenging since similar planes can appear substantially different; for instance, carpets come in various types and colors. To address this inherent uncertainty in vision-based surface classification, this study first generates a new, unique data set composed of 2,081 surface images (carpet, tiles, and wood) captured in different indoor environments. Secondly, the pre-trained state-of-the-art deep learning models, namely InceptionV3, VGG16, VGG19, ResNet50, Xception, InceptionResNetV2, and
MobileNetV2, were utilized to recognize the surface type. Additionally, a lightweight
MobileNetV2-modified model was proposed for surface classification. The proposed model has approximately four times fewer total parameters than the original
MobileNetV2 model, reducing the size of the trained model weights from 42 MB to 11 MB. Thus, the proposed model can be used in robotic systems with limited computational capacity and embedded systems. Lastly, several optimizers, such as SGD, RMSProp, Adam, Adadelta, Adamax, Adagrad, and Nadam, are applied to distinguish the most efficient network. Experimental results demonstrate that the proposed model outperforms all other applied methods and existing approaches in the literature by achieving 99.52% accuracy and an average score of 99.66% in precision, recall, and F1-score. In addition to this, the proposed lightweight model was tested in real-time on a mobile robot in 11 scenarios consisting of various indoor environments such as offices, hallways, and homes, resulting in an accuracy of 99.25%. Finally, each model was evaluated in terms of model loading time and processing time. The proposed model requires less loading and processing time than the other models.