In the field of deep learning-based object detection algorithms, the YOLO algorithm has attracted a lot of attention due to its advantages in speed and accuracy. However, there are problems such as low detection speed and poor detection accuracy in the application of UAV images. For this reason, an improved small object detection algorithm MTF-YOLOv5 based on the YOLOv5 algorithm is proposed. Firstly, MobileNet V3 is used to replace the main network to reduce the amount of network parameters and improve the running speed. Secondly, a small object detection layer is added to the original network structure to enhance the detection ability for small objects. Finally, the FocalModulation is introduced to replace the SPP module to improve accuracy. The improved algorithm is tested on the processed TinyPerson dataset. Experimental results show that the mean average precision (mAP) on the YOLOv5s algorithm improved by 4.1%, and the FPS increased from 86 to 136, improving the speed by 51%. While maintaining detection speed, the updated algorithm successfully raises detection accuracy.
Hyperspectral unmixing (HU) in hyperspectral image (HSI) processing is a crucial step. However, the accuracy of unmixing methods is limited by the variability in endmember and the complexity of the HSI structure found in natural scenes. Endmember variability refers to the variations or differences exhibited by endmembers in different locations or under varying conditions within a hyperspectral remote sensing scene. Therefore, to enhance the accuracy of unmixing results, it is crucial to fully leverage spectral, geometric, and spatial information within HSIs, comprehensively exploring the spectral characteristics of endmembers. We present a cascaded dual-constrained transformer autoencoder (AE) for HU with endmember variability and spectral geometry. The model utilizes a transformer AE network to extract the global spatial features in the HSI. Additionally, it incorporates the minimum distance constraint to account for the geometric information of the HSI. Given the similarity in shape exhibited by endmembers of each individual material, with the primary endmember variability being expressed through overall intensity fluctuations, an abundance-weighted constraint method for endmember spectral angle distance is proposed. During training, the architecture utilizes two cascaded networks to preserve the detailed information in the HSI. We evaluate the proposed model using three real datasets. The experimental results indicate that the proposed method achieves superior performance in abundance estimation and endmember extraction. Furthermore, the effectiveness of the two constraint methods was verified through ablation experiments.
To address the problems of current small target detection algorithms such as slow detection speed, high memory consumption, and not well applied to mobile devices, an improved lightweight small target detection algorithm MTG-YOLOv5 is proposed based on YOLOv5s algorithm. firstly, MobileNet V3 is used instead of the backbone network to reduce the number of network parameters and increase the speed of operation. Secondly, the small target detection layer is added to the original network structure to enhance the detection capability of small targets. Finally, Ghost module is introduced to replace the commonly used convolutional kernel to ensure detection accuracy and further light weighting. The improved algorithm was tested on the processed TinyPerson dataset. The experimental results show that the overall average accuracy of mAP of YOLOv5s algorithm is improved by 4.5%, the parameters are reduced by 1/2, the FPS is improved from 123.54 to 187.62, and the speed is enhanced by 51.87%. The algorithm effectively improves the detection speed while ensuring the detection accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.