基于YOLOv3的自建数据集实时鱼类检测方法

Ali Amin, Salmeen Bahnasy, K. Elghamry, A. Samir, A. Emad, M. Darweesh, A. El-Sherif
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引用次数: 2

摘要

创建一个模型来实时检测水下自由移动的鱼类是一个具有挑战性的过程,主要有两个原因。首先,可用的数据集受到一些限制,严重影响了在具有挑战性和模糊的环境中运行的检测模型的结果。这些模型应该能够捕捉到所有的鱼在不同环境下的运动。其次,从具有较高的精度和令人满意的帧数/秒(FPS)两方面选择符合期望要求的便捷检测模型系统。为了克服第一个挑战,通过从视频中提取1800帧并手动注释它们来创建一个新的数据集,以克服不同的背景问题以及鱼的复杂运动和方向。对于第二个挑战,经过对不同目标检测系统的比较,我们选择了YOLOv3,因为它在其他系统中具有较高的精度。该方法以mAP(平均精度)作为准确度度量,得分为76.81%,以F-score作为准确度度量,得分为89.17%,被认为是文献中准确率最高的结果之一。此外,模型速率为12 FPS,满足实时性要求。
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Real-Time Fish Detection Approach on Self-Built Dataset Based on YOLOv3
Creating a model to detect freely moving fish underwater in real-time is a challenging process for two main reasons. First, the available datasets suffer from some limitations that severely affect the results of the detection models operating in challenging and blurry environments. These models should be able to capture all of the fish movement given different types of surroundings. Second, choosing the convenient detection model system which matches the desired requirements from having high accuracy with satisfying frames per second (FPS). To overcome the first challenge, a new dataset was created by extracting 1800 frames from videos and manually annotating them to overcome the different background issues and the complex movements and orientations of the fish. Regarding the second challenge and after comparing between different object detection systems, YOLOv3 was chosen as it proved to achieve high accuracy among other systems. The proposed approach scored 76.81% using (mean average precision) mAP as an accuracy metric and 89.17% using F-score, which is considered one of the most accurate outcomes among the literature. Moreover, the model rate is 12 FPS which is satisfying for real-time.
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