Sarkar Pratima, De Sourav, Gurung Sandeep, Dey Prasenjit
Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Rangpo, Sikkim, 737136, India.
Department of Computer Science and Engineering, Techno International New Town, Kolkata, 700156, India.
Sci Rep. 2024 Sep 28;14(1):22448. doi: 10.1038/s41598-024-73243-9.
Underwater object detection is a crucial aspect of monitoring the aquaculture resources to preserve the marine ecosystem. In most cases, Low-light and scattered lighting conditions create challenges for computer vision-based underwater object detection. To address these issues, low-colorfulness and low-light image enhancement techniques are explored. This work proposes an underwater image enhancement technique called Underwater Image Colorfulness Enhancement MIRNet (UICE-MIRNet) to increase the visibility of small, multiple, dense objects followed by underwater object detection using YOLOv4. UICE-MIRNet is a specialized version of classical MIRNet, which handles random increments of brightness features to address the visibility problem. The proposed UICE-MIRNET restrict brightness and also works on the improvement of the colourfulness of underwater images. UICE-MIRNet consists of an Underwater Image-Colorfulness Enhancement Block (UI-CEB). This block enables the extraction of low-colourful areas from underwater images and performs colour correction without affecting contextual information. The primary characteristics of UICE-MIRNet are the extraction of multiple features using a convolutional stream, feature fusion to facilitate the flow of information, preservation of contextual information by discarding irrelevant features and increasing colourfulness through proper feature selection. Enhanced images are then trained using the YOLOv4 object detection model. The performance of the proposed UICE-MIRNet method is quantitatively evaluated using standard metrics such as UIQM, UCIQE, entropy, and PSNR. The proposed work is compared with many existing image enhancement and restoration techniques. Also, the performance of object detection is assessed using precision, recall, and mAP. Extensive experiments are conducted on two standard datasets, Brackish and Trash-ICRA19, to demonstrate the performance of the proposed work compared to existing methods. The results show that the proposed model outperforms many state-of-the-art techniques.
水下目标检测是监测水产养殖资源以保护海洋生态系统的关键环节。在大多数情况下，低光照和散射光照条件给基于计算机视觉的水下目标检测带来了挑战。为了解决这些问题，人们探索了低色彩度和低光照图像增强技术。这项工作提出了一种名为水下图像色彩增强MIRNet（UICE-MIRNet）的水下图像增强技术，以提高小尺寸、多个密集目标的可见性，随后使用YOLOv4进行水下目标检测。UICE-MIRNet是经典MIRNet的一个专门版本，它处理亮度特征的随机增量以解决可见性问题。所提出的UICE-MIRNET限制亮度，并且还致力于改善水下图像的色彩度。UICE-MIRNet由一个水下图像色彩增强模块（UI-CEB）组成。该模块能够从水下图像中提取低色彩度区域，并在不影响上下文信息的情况下进行色彩校正。UICE-MIRNet的主要特点是使用卷积流提取多个特征、进行特征融合以促进信息流动、通过丢弃无关特征来保留上下文信息以及通过适当的特征选择来增加色彩度。然后使用YOLOv4目标检测模型对增强后的图像进行训练。使用诸如UIQM、UCIQE、熵和PSNR等标准指标对所提出的UICE-MIRNet方法的性能进行定量评估。将所提出的工作与许多现有的图像增强和恢复技术进行比较。此外，使用精度、召回率和平均精度均值（mAP）来评估目标检测的性能。在两个标准数据集Brackish和Trash-ICRA19上进行了广泛的实验，以证明所提出的工作与现有方法相比的性能。结果表明，所提出的模型优于许多现有技术。
