论文标题
朝着更有效的效率和低光实时海洋碎片检测
Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detection
论文作者
论文摘要
海洋碎片是海洋环境的健康和人类健康的一个问题，因为随着时间的流逝，由于碎片分解而导致的一小块塑料被称为“微塑料”，这在任何水平上都进入了食物链。对于海洋碎屑检测和去除，自动水下车辆（AUV）是潜在的解决方案。在这封信中，我们着重于实时和低光对象检测的AUV愿景的效率。首先，我们提高了一类最先进的对象探测器的效率，即有效的D0，D0上的AP，D1上的2.6％AP，D2上的D2 AP和1.3％的AP和D3的1.3％AP，而无需增加GPU延迟。随后，我们创建并公开提供了一个数据集，用于检测水中塑料袋和瓶子，并在此和另一个数据集上培训了我们改进的有效数据集以检测海洋碎片。最后，我们研究了检测器的性能如何受到低光条件的影响，并在准确性和延迟方面比较了两种低光的水下图像增强策略。源代码和数据集公开可用。
Marine debris is a problem both for the health of marine environments and for the human health since tiny pieces of plastic called "microplastics" resulting from the debris decomposition over the time are entering the food chain at any levels. For marine debris detection and removal, autonomous underwater vehicles (AUVs) are a potential solution. In this letter, we focus on the efficiency of AUV vision for real-time and low-light object detection. First, we improved the efficiency of a class of state-of-the-art object detectors, namely EfficientDets, by 1.5% AP on D0, 2.6% AP on D1, 1.2% AP on D2 and 1.3% AP on D3 without increasing the GPU latency. Subsequently, we created and made publicly available a dataset for the detection of in-water plastic bags and bottles and trained our improved EfficientDets on this and another dataset for marine debris detection. Finally, we investigated how the detector performance is affected by low-light conditions and compared two low-light underwater image enhancement strategies both in terms of accuracy and latency. Source code and dataset are publicly available.
