YOLO-RS: Remote Sensing Enhanced Crop Detection Methods

With the rapid development of remote sensing technology, crop classification and health detection based on deep learning have gradually become a research hotspot. However, the existing target detection methods show poor performance when dealing with small targets in remote sensing images, especially in the case of complex background and image mixing, which is difficult to meet the practical application requirementsite. To address this problem, a novel target detection model YOLO-RS is proposed in this paper. The model is based on the latest Yolov11 which significantly enhances the detection of small targets by introducing the Context Anchor Attention (CAA) mechanism and an efficient multi-field multi-scale feature fusion network. YOLO-RS adopts a bidirectional feature fusion strategy in the feature fusion process, which effectively enhances the model's performance in the detection of small targets. Small target detection. Meanwhile, the ACmix module at the end of the model backbone network solves the category imbalance problem by adaptively adjusting the contrast and sample mixing, thus enhancing the detection accuracy in complex scenes. In the experiments on the PDT remote sensing crop health detection dataset and the CWC crop classification dataset, YOLO-RS improves both the recall and the mean average precision (mAP) by about 2-3\% or so compared with the existing state-of-the-art methods, while the F1-score is also significantly improved. Moreover, the computational complexity of the model only increases by about 5.2 GFLOPs, indicating its significant advantages in both performance and efficiency. The experimental results validate the effectiveness and application potential of YOLO-RS in the task of detecting small targets in remote sensing images.

A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition

The deployment of neural networks in vehicle platforms and wearable Artificial Intelligence-of-Things (AIOT) scenarios has become a research area that has attracted much attention. With the continuous evolution of deep learning technology, many image classification models are committed to improving recognition accuracy, but this is often accompanied by problems such as large model resource usage, complex structure, and high power consumption, which makes it challenging to deploy on resource-constrained platforms. Herein, we propose an ultra-lightweight binary neural network (BNN) model designed for hardware deployment, and conduct image classification research based on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. In addition, we also verify it on the Chinese Traffic Sign (CTS) and Belgian Traffic Sign (BTS) datasets. The proposed model shows excellent recognition performance with an accuracy of up to 97.64%, making it one of the best performing BNN models in the GTSRB dataset. Compared with the full-precision model, the accuracy loss is controlled within 1%, and the parameter storage overhead of the model is only 10% of that of the full-precision model. More importantly, our network model only relies on logical operations and low-bit width fixed-point addition and subtraction operations during the inference phase, which greatly simplifies the design complexity of the processing element (PE). Our research shows the great potential of BNN in the hardware deployment of computer vision models, especially in the field of computer vision tasks related to autonomous driving.