the learning rate adjustment strategy of Adam optimizer and cosine annealing is chosen, where the initial learning rate is set to 1e-3 and the minimum learning rate is 1e-5, which makes periodic changes accordin
ACE dehazing algorithm flowchart. Full size image Select some drilling field images for comparative experiments, and the comparison results are shown in the Fig. 2. For scenes with large dust and mist (a) and (c), the ACE algorithm can clearly eliminate the dust and mist effect in the imag...
Aiming at the problem of low fall detection accuracy and poor real-time performance in indoor scenes due to the effects of light change, occlusion of the human body form, and changes in the human body posture under special viewpoint, a lightweight improve...
Key Features of Predict Mode YOLOv8's predict mode is designed to be robust and versatile, featuring: Multiple Data Source Compatibility:Whether your data is in the form of individual images, a collection of images, video files, or real-time video streams, predict mode has you covered. Stream...
Download: Download full-size image Figure 11. Target detection accuracy comparison The detection results of the YOLO-MFD (left) and the YOLOv8 (right) are shown below. 5. Conclusions This paper introduces a novel algorithm designed for remote sensing image object detection. YOLO-MFD is an algor...
Download: Download full-size image Algorithm 1. WDA Module. 4.4. Loss Functions In YOLOT, three categories of loss functions are employed: the loss associated with classification, the loss related to localization, and the loss pertaining to confidence. The classification loss function calculates the...
We welcome contributions in the form of pull requests. To streamline the review process, please follow these guidelines: Fork the repository: Fork the Ultralytics YOLO repository to your GitHub account. Create a branch: Create a new branch in your forked repository with a descriptive name for ...
We note that our algorithm allows predictions that are larger than the underlying receptive field. (我们允许预测结果比接受阈大) Such predictions are not impossible—one may still roughly infer the extent of an object if only the middle
YOLOv7-CWFD detection algorithm In this paper, the YOLOv7-CWFD (CSDPAN-Weighted Loss-FFCAM-DySample) is proposed to improve the YOLOv7 model for problems of large size, low detection accuracy, and information loss in the upsampling process. The network structure is illustrated in Fig. 1....
Full size image In response to the need for detecting small objects in aerial and drone imagery, we propose the MPE-YOLO algorithm to adjust the structure of the original YOLOv8 components. As shown in Fig.2, by designing the multilevel feature integrator (MFI) module, the representation and...