Yingkun Song¹, Shunhe Hong¹, Chentao Hu¹, Pingan He², Lingbing Tao¹, Zhixin Tie^1,3,*, Chengfu Ding⁴

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5761-5784, 2023, DOI:10.32604/cmc.2023.038910

Abstract Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems, and road target detection is one of the most difficult tasks in the field of computer vision. The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments. However, because road targets are prone to complicated backgrounds and sparse features, it is challenging to detect and identify vehicle kinds fast and reliably. We suggest a new vehicle detection model called MEB-YOLO, which combines Mosaic and MixUp data augmentation, Efficient Channel Attention (ECA) attention mechanism, Bidirectional Feature Pyramid Network (BiFPN)… More >

Man-Ting Li, Sang-Hyun Lee^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6155-6165, 2022, DOI:10.32604/cmc.2022.025714

Abstract This study aims to detect and prevent greening disease in citrus trees using a deep neural network. The process of collecting data on citrus greening disease is very difficult because the vector pests are too small. In this paper, since the amount of data collected for deep learning is insufficient, we intend to use the efficient feature extraction function of the neural network based on the Transformer algorithm. We want to use the Cascade Region-based Convolutional Neural Networks (Cascade R-CNN) Swin model, which is a mixture of the transformer model and Cascade R-CNN model to detect greening disease occurring in… More >