Abstract
The complex marine environment exacerbates the challenges of object detection manifold. Marine trash endangers the aquatic ecosystem, presenting a persistent challenge. Accurate detection of marine deposits is crucial for mitigating this harm. Our work addresses underwater object detection by enhancing image quality and evaluating detection methods. We use Detectron2's backbone with various base models and configurations for this task. We propose a novel channel stabilization technique alongside a simplified image enhancement model to reduce haze and color cast in training images, improving multi-scale object detection. Following image processing, we test different Detectron2 backbones for optimal detection accuracy. Additionally, we apply a sharpening filter with augmentation techniques to highlight object profiles for easier recognition. Results are demonstrated on the TrashCan Dataset, both instance and material versions. The best-performing backbone method incorporates our channel stabilization and augmentation techniques. We also compare our Detectron2 detection results with the Deformable Transformer. In the instance version of TrashCan 1.0, our method achieves a 9.53% absolute increase in average precision for small objects and a 7% absolute gain in bounding box detection compared to the baseline. The code will be available on Code: this https URL Object-Detection-via-Channel-Stablization
Abstract (translated)
复杂的海域环境加剧了目标检测多维的挑战。海洋垃圾对水生生态系统构成持久性的威胁，带来了一个关键的挑战。准确地检测海洋沉积物对于减轻这种危害至关重要。我们的工作通过提高图像质量并评估检测方法来解决水下目标检测。我们使用Detectron2的骨干网络以及各种基础模型和配置来处理这个任务。我们提出了一种新的通道稳定技术，并在简单的图像增强模型旁边，用于减少训练图像中的雾和色彩差，从而提高多尺度目标检测。在图像处理后，我们测试了不同的Detectron2骨干网络以获得最佳的检测精度。此外，我们使用增强技术应用模糊滤波器来突出对象的轮廓，以方便识别。结果在TrashCan数据集的实例和材料版本上都得到了展示。最佳表现的特征集包含了我们的通道稳定和增强技术。我们还比较了Detectron2检测结果与Deformable Transformer。在TrashCan 1.0的实例版本中，我们的方法实现了小物体平均精度9.53%的绝对增加和边界框检测7%的绝对增加，与基线相比。代码将在此处公布：https://this URL Object-Detection-via-Channel-Stabilization
URL
https://arxiv.org/abs/2408.01293
