Improved YOLOv3 Algorithm for Ship Target Detection

To solve the problem of ship target detection in complex surface environment, a real-time Darknet-53 network model based on a deep learning framework is incorporated with the improved YOLOv3 algorithm. This method combines a Soft Non-Maximum Suppression (Soft-NMS) algorithm and a Frequency-Tuned (FT) salient region detection algorithm to detect the features of ships on water. By replacing the original NMS algorithm with Soft-NMS algorithm, the detection effect of the algorithm for small target and overlapping target is improved obviously; Incorporates the saliency region features obtained by the FT algorithm based on frequency adjustment. The saliency region features obtained by FT contain more overall information of the target. Experimental results demonstrate that compared with the original YOLOv3, the detection accuracy and speed of the proposed method are considerably improved.

[1]  Sabine Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Junmei Liu,et al.  An Improved BING/NMS Algorithm for Aircraft Detection , 2019 .

[3]  Dumitru Erhan,et al.  Scalable Object Detection Using Deep Neural Networks , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[5]  Larry S. Davis,et al.  Soft-NMS — Improving Object Detection with One Line of Code , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[6]  Jiang Gangyi,et al.  Review on Vehicle Detection and Tracking Techniques Based on Video Processing in Intelligent Transportation Systems , 2005 .

[7]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2015, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Luc Van Gool,et al.  Efficient Non-Maximum Suppression , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[11]  Dumitru Erhan,et al.  Deep Neural Networks for Object Detection , 2013, NIPS.

[12]  Ming-Hsuan Yang,et al.  Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.