In the past few decades, many efficient object detectors have been proposed for natural scene image object detection. However, due to the complex scenes and high interclass similarity of optical remote sensing (RS) images, applying these detectors to optical RS images directly is not very effective. Most of the recent detectors pursue higher accuracy while ignoring the balance between detection accuracy and speed, which hinders the practical application of these detectors, especially in embedded devices. To meet these challenges, a fast and accurate detector based on YOLO (You Only Look Once) with decoupled attention head (YOLO-DA) is proposed, which effectively improves detection performance while only introducing minimal complexity. Specifically, an attention module at the end of the detector is designed for guiding a neural network to extract more efficient features from the complex background while also minimizing the amount of additional computation. Moreover, a lightweight decoupled detection head with enhanced classification and localization capability is developed to detect objects with high interclass similarity. In the experiments, the proposed method effectively solves the problem of high interclass similarity and improves the mAP by 6.8% on the fine-grained optical RS dataset SIMD, compared with YOLOv5-L. In addition, the proposed method improves the mAP by 1.0%, 1.7% and 0.6% on the other three publicly open optical RS datasets, respectively. Experimental results on detection accuracy and inference time demonstrate that our method achieves the best trade-off between detection performance and speed.

In recent years, driver fatigue has become one of the most important factors for traffic accidents, which has come at a great cost to the safety and property of drivers and pedestrians. Researchers have proposed many fatigue detection methods to solve this problem, which can be divided into three types: ¹ physiological parameters, vehicle behaviors, and facial feature analysis. The first method measures the driver’s physiological parameters² 3. 4. 5. ^{– 6} by using tools such as electroencephalogram and electrocardiogram. However, these methods are invasive and require contact with the driver’s body. The second method is used to measure the behaviors of vehicles,⁷ 8. 9. 10. ^{– 11} such as speed, steering wheel rotation angle, and lane departure detection; however, this method is affected by driving conditions, driving experience, and vehicle type. The third method analyzes the driver’s face, such as the PERCLOS value, blink frequency, head posture, and yawn detection. PERCLOS is the abbreviation of percentage of eyelid closure over the pupil over time, which is the percentage of the closing time of the eye over a specific period of time. This method is noninvasive and easy to implement and is applied to the fatigue detection in this paper. The following is a brief introduction to some of the algorithms for detecting fatigue by facial feature analysis.