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8. Traffic Sign Detection Under Adverse Environmental Conditions Based on CNN

Author

Qiang Gao, Huiping Hu, and Wei Liu

Subjects
CNN, adverse environments, image enhancement, TSDR, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

A strong and reliable Traffic Sign Detection and Recognition (TSDR) system is essential for the effective deployment of autonomous driving technology. In this field, numerous scholars have conducted extensive research, but current studies only consider TSDR under ideal conditions, neglecting scenarios such as rain, snow, fog, which can cause image blurring. This paper investigates the challenges of TSDR performance degradation caused by five adverse environmental conditions: rain, snow, fog, lens dirt, and lens blur. To overcome the adverse effects of these conditions on TSDR, this paper proposes a Convolutional Neural Network (CNN)-based TSDR method, consisting of three modules: adverse environment classification module, image enhancement module, and traffic sign detection module. The adverse environment classifier, based on the VGG19 architecture, identifies whether the image includes the aforementioned five adverse weather conditions. The image enhancement module named Enhance-Net enhances each of the five adverse environments separately and specifically enhances the traffic sign regions within the image, rather than the entire image area. To increase the speed of the proposed method, the traffic sign detection module utilizes the YOLOv4 framework. The proposed method’s effectiveness is assessed using the CURE-TSD dataset, which includes traffic videos recorded in various adverse environmental conditions. Experimental results demonstrate that under five different levels of adverse environments, the pro-posed method achieves 95.03% accuracy and runs at a rate of 12.79 fps (frames per second). In contrast to the current benchmark, although there is a 2.81% reduction in accuracy resulting from training the proposed method on a subset of the dataset, the speed has increased by 12.03 fps, demonstrating the efficacy of the proposed approach.

Published
2024
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9. A Prediction Model for Soybean Meal Futures Price Based on CNN-ILSTM-SA

Author

Haitao Liu, Yang Pan, Wei Liu, and Jingyang Wang

Subjects
CNN, ILSTM, self-attention mechanism, soybean meal futures, price prediction, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Predicting the price of soybean meal futures has always been a hot topic in the financial market. It is influenced by many factors and has complex nonlinear characteristics, which makes it difficult to predict accurately. This paper proposes a prediction model for soybean meal futures price based on CNN-ILSTM-SA, which consists of CNN (Convolutional Neural Network), ILSTM (Improved Long Short-Term Memory), and SA (Self-Attention mechanism). CNN can well perform feature extraction on the input time series data. The ILSTM model presented in this paper removes the output gate of LSTM (Long Short-Term Memory) and improves the forget gate and input gate, thus reducing ILSTM’s complexity, shortening training time, and improving learning ability. SA can get the correlation between different input terms and predicted values, and assign different weight coefficients according to the correlation level to improve prediction accuracy. To verify the CNN-ILSTM-SA’s effectiveness, we compare it with eighteen baseline models, and the experimental results show that it is optimal. The MAE (Mean Absolute Error) of CNN-ILSTM-SA is 39.0441, the smallest of all models. As the data of the soybean meal futures price fluctuates in the range of 3500-4500 in experiments, the proportion of the MAE value of 39.0441 is 0.87%-1.15%, which is at an extremely low level; $\mathrm {R}^{2}$ (R-square) is 0.97487, which is closest to 1; The training time is 157.64s.

Published
2024
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