Convolutional Neural Networks Adapted for Regression Tasks: Predicting the Orientation of Straight Arrows on Marked Road Pavement Using Deep Learning and Rectified Orthophotography

The growth of artificial intelligence has led to the widespread use of convolutional neural networks (CNNs) for computer vision applications, traditionally for binary and categorical classification tasks. However, there remains untapped potential for advancing computer vision through deep learning in regression tasks. Design engineers across many disciplines use computer-aided design software to model their designs. These computer-integrated designs often require machinery for construction or fabrication. For many engineering designs, precision and tolerancing is essential for the proper function and performance of the design. The engineering process typically involves manual testing and parameter measurements to ensure the proper function of the design before it is marketed. However, training a neural network to automate these tests and provide accurate numeric estimates of system parameters without manual intervention can significantly increase efficiency and decrease the time to market for many products. This shift from manual to automated testing allows for a heightened focus on innovation and project development while minimizing the time and resource dedication for validation. This article outlines the implementation of CNN models designed to enhance the efficiency of manually validating engineered projects. Our approach involves utilizing computer-aided design simulation image captures as training data for our pipeline. We integrate a real-time color-filtering and fiducial rotation scaling normalization process on any fabricated design image. Through these pre-processing methods, our algorithm can perceive these images in a consistent manner with simulation images from the model training.Our current model is trained with only 1020 simulation images and achieves a 1.99% average training prediction error on this dataset after training. Before, our errors were a 10.51% average error in our initial model implementation and 3.63% in our second implementation. On our test set, consisting of six captured and preprocessed sample fabricated design images, our model achieves a 3.40% average prediction error. The performance of a regression label neural network of this nature depends largely on the amount and range of data and simulation scenarios considered. As we continue to expand our training dataset through an optimized pipeline, we anticipate a significant improvement in model performance.

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TLDM: An Enhanced Traffic Light Detection Model Based on YOLOv5

Song,

Hu,

Gong

et al. 2024

Electronics

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Traffic light detection and recognition are crucial for enhancing the security of unmanned systems. This study proposes a YOLOv5-based traffic light-detection algorithm to tackle the challenges posed by small targets and complex urban backgrounds. Initially, the Mosaic-9 method is employed to enhance the training dataset, thereby boosting the network’s ability to generalize and adapt to real-world scenarios. Furthermore, the Squeeze-and-Excitation (SE) attention mechanism is incorporated to improve the network. Moreover, the YOLOv5 algorithm’s loss function is optimized by substituting it with Efficient Intersection over Union loss (EIoU_loss), which addresses issues like missed detection and false alarms. Experimental results demonstrate that the model trained with this enhanced network achieves an mAP (mean average precision) of 99.4% on a custom dataset, which is 6.3% higher than that of the original YOLOv5, while maintaining a detection speed of 74 f/s. Therefore, this algorithm offers higher detection accuracy and effectively meets real-time operational requirements. The proposed method has a strong application potential, and can be widely used in the field of automatic driving, assisted driving, etc. Its application is not only of great significance in improving the accuracy and speed of traffic sign detection, but also can provide technical support for the development of intelligent transportation systems.

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Convolutional Neural Networks Adapted for Regression Tasks: Predicting the Orientation of Straight Arrows on Marked Road Pavement Using Deep Learning and Rectified Orthophotography

Cited by 4 publications

References 34 publications

Evaluation of Multiclass Extraction of Representative Road Lines Found on Highway Pavement Using Supervised Semantic Segmentation Techniques and Aerial Imagery

Evaluation of Multiclass Extraction of Representative Road Lines Found on Highway Pavement Using Supervised Semantic Segmentation Techniques and Aerial Imagery

Convolutional neural networks for engineering design validation

TLDM: An Enhanced Traffic Light Detection Model Based on YOLOv5

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