Detection and Tracking of Water Columns at Marine Impact Points Based on Dynamic Features

doi:10.12382/bgxb.2024.0918

Abstract

Abstract:

The effective detection and tracking of water columns at marine impact points using visible light images is key to automatically check a target at sea. The existing detection and tracking algorithms still have a high false alarm rate and identity switch times (IDs) due to the movement of camera, the adjustment of focal length, and the changes of water columns. To solve the above problems, this paper proposes a detection and tracking algorithm based on dynamic features for water columns at marine impact points. The YOLOv8 target detector is used to detect the static water columns, and a small target detection head is added to the shallow feature map to enhance the model’s ability to detect small water columns. An improved ByteTrack tracker is used to track the water columns, and the tracking offsets caused by camera movement is compensated by combining camera movement and Kalman filtering. And then, a support vector machine is used for comprehensive decision-making to judge the water columns according to the spatiotemporal features of the water columns formation stage. Compared with traditional detection and tracking algorithms, the proposed algorithm is used to improve the three key performance indicators of multiple object tracking accuracy (MOTA), identification F1 (IDF1), and multiple object tracking precision (MOTP) by 7.8%, 5.1%, and 0.9%, respectively, the number of false positives (FP) is reduced by 112 times, and the numbers of IDs and false detections are both reduced to zero. Experimental results show that the proposed algorithm can not only accurately detect and track the water columns but also effectively exclude other interfering factors, thus achieving a significant enhancement in overall performance.

Key words: dynamic feature, motion estimation, target detection, target tracking, ByteTrack

CLC Number:

TJ301

GUI Fan, SHI Zhangsong, SUN Shiyan, YING Wenjian, HU Weiqiang, XU Huihui, WU Zhonghong, HU Qingping, ZHANG Jun. Detection and Tracking of Water Columns at Marine Impact Points Based on Dynamic Features[J]. Acta Armamentarii, 2025, 46(5): 240918-.

Figures/Tables 13

Fig.1 Detection and tracking algorithm framework for water columns based on dynamic features

Fig.2 YOLOv8-small detection model structure diagram

Fig.3 Water column detection box mapping based on CME

Fig.4 Changing curve of target area over time

Fig.5 Variation of target average growth rate with sequence number

Table 1 Test set

视频序号	视角	分辨率	帧率	时长	水柱个数
001	水面	640×352	21	3min18s	21
002	空中	1920×1080	30	2min25s	10
003	空中	1920×1080	50	2min59s	10

Table 2 Performance comparison of the algorithm

算法	P/%	R/%	mAP50/%	mAP50-95/%
YOLOv8	0.939	0.887	0.948	0.799
YOLOv8-samll	0.953	0.915	0.973	0.817

Table 3 Comparison of detected results before and after improvement

Table 4 Detection and tracking results of water columns based on static features

视频序号与均值	IDF1/%	GT	MT	FP	FN	IDs	MOTA/%	MOTP/%	检靶数	误检数	漏检数
001	98.4	21	21	5	7	0	96.8	19.7	21	5	0
002	90.2	10	10	75	1	5	88.9	7.0	10	7	0
003	91.4	10	10	56	4	0	81.3	7.5	10	11	0
均值	93.3						89	11.4

Table 5 Detection and tracking results of water columns based on dynamic features

视频序号及均值	IDF1/%	GT	MT	FP	FN	IDs	MOTA/%	MOTP/%	检靶数	误检数	漏检数
001	98.8	21	21	2	7	0	97.6	19.7	21	0	0
002	98.3	10	10	12	13	0	96.6	9.9	10	0	0
003	98.1	10	10	8	4	0	96.3	7.5	10	0	0
均值	98.4						96.8	12.3

Table 6 Comparison of tracking effects for test video 001

Table 7 Comparison of tracking effects for test video 002

Table 8 Comparison of tracking effects for test video 003

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