Uneingeschränkter Zugang

Improved YOLOv5s Algorithm for Target Detection in Highlighted Field Camera Surveillance Images


Zitieren

Mohan, A., Singh, A. K., Kumar, B., & Dwivedi, R. (2020). Review on remote sensing methods for landslide detection using machine and deep learning. Transactions on Emerging Telecommunications Technologies(4). Search in Google Scholar

Rabbi, J., Ray, N., Schubert, M., Chowdhury, S., & Chao, D. (2020). Small-object detection in remote sensing images with end-to-end edge-enhanced gan and object detector network. Remote Sensing, 12(9), 1432. Search in Google Scholar

Zhang, W., Jin, S., Zhou, L., Xie, X., Wang, F., & Jiang, L., et al. (2022). Multi-feature embedded learning svm for cloud detection in remote sensing images. Computers and Electrical Engineering. Search in Google Scholar

Wang, C., Liu, H., Shen, Y., Zhao, K., Xing, H., & Wu, H. (2020). High-resolution remote-sensing image-change detection based on morphological attribute profiles and decision fusion. Complexity, 2020. Search in Google Scholar

Gang, W., Jinyong, C., Feng, G., & Jinliang, W. U. (2018). Research on the infrastructure target detection of remote sensing image based on deep learning. Radio Engineering. Search in Google Scholar

Zhang, Q., Cong, R., Li, C., Cheng, M. M., & Kwong, S. (2020). Dense attention fluid network for salient object detection in optical remote sensing images. IEEE Transactions on Image Processing, 1-1. Search in Google Scholar

Rangasamy, V., & Subramaniam, S. (2017). Framelet transform based edge detection for straight line detection from remote sensing images. Kuwait Journal of ence, 44(1), 78-85. Search in Google Scholar

Lal, A. M., & Anouncia, S. M. (2017). Adapted sparse fusion with constrained clustering for semisupervised change detection in remotely sensed images. Journal of Applied Remote Sensing, 11(1), 016013. Search in Google Scholar

Mingalev, A. V., Belov, A. V., Gabdullin, I. M., Mardanova, D. A., Agafonova, R. R., & Shusharin, S. N., et al. (2022). Evaluating and testing neural-network algorithm capabilities for automating image data analysis for remote sensing of the earth. Journal of optical technology. Search in Google Scholar

Zhong, S., Zhou, H., Cui, X., Cao, X., Zhang, F., & Duan, J. (2023). Infrared small target detection based on local-image construction and maximum correntropy. Measurement, 211. Search in Google Scholar

Cheng, B., Li, Z., Xu, B., Yao, X., Ding, Z., & Qin, T. (2021). Structured object-level relational reasoning cnn-based target detection algorithm in a remote sensing image. Remote Sensing, 13(2), 281. Search in Google Scholar

He, K., Zhou, D., Zhang, X., Nie, R., Wang, Q., & Jin, X. (2017). Infrared and visible image fusion based on target extraction in the nonsubsampled contourlet transform domain. Journal of Applied Remote Sensing, 11(1), 015011. Search in Google Scholar

Li, C., Gao, H., Yang, Y., Qu, X., & Yuan, W. (2019). Segmentation method of high-resolution remote sensing image for fast target recognition. International Journal of Robotics & Automation(3), 34. Search in Google Scholar

Liao, K., Fan, B., Zheng, Y., Lin, G., & Cao, C. (2020). Bow image retrieval method based on ssd target detection. IET Image Processing, 14. Search in Google Scholar

Chen, D., Sun, S., Lei, Z., Shao, H., & Wang, Y. (2021). Ship target detection algorithm based on improved yolov3 for maritime image. Journal of Advanced Transportation, 2021(10), 1-11. Search in Google Scholar

Tian, L., Cao, Y., He, B., Zhang, Y., & Li, D. (2021). Image enhancement driven by object characteristics and dense feature reuse network for ship target detection in remote sensing imagery. Remote Sensing, 13(7), 1327. Search in Google Scholar

Yanchen, W. (2021). Sonar image target detection and recognition based on convolution neural network. Mobile Information Systems, 2021(6), 1-8. Search in Google Scholar

Zou, FuhaoXiao, WeiJi, WantingHe, KunkunYang, ZhixiangSong, JingkuanZhou, HelenLi, Kai. (2020). Arbitrary-oriented object detection via dense feature fusion and attention model for remote sensing super-resolution image. Neural computing & applications, 32(18). Search in Google Scholar

eISSN:
2444-8656
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
Volume Open
Fachgebiete der Zeitschrift:
Biologie, andere, Mathematik, Angewandte Mathematik, Allgemeines, Physik