Underwater Object Detection Enhancement via Channel Stabilization

Document Type

Conference Proceeding

Publication Title

2022 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2022


The complex marine environment exacerbates the challenges of object detection manifold. With the advent of the modern era, marine trash presents a danger to the aquatic ecosystem, and it has always been challenging to address this issue with complete grip. Therefore, there is a significant need to precisely detect marine deposits and locate them accurately in challenging aquatic surroundings. To ensure the safety of the marine environment caused by waste, the deployment of underwater object detection is a crucial tool to mitigate the harm of such waste. Our work explains the image enhancement strategies used and experiments exploring the best detection obtained after applying these methods. Specifically, we evaluate Detectron 2's backbones performance using different base models and configurations for the underwater detection task. We first propose a channel stabilization technique on top of a simplified image enhancement model to help reduce haze and colour cast in training images. The proposed procedure shows improved results on multi-scale size objects present in the data set. After processing the images, we explore various backbones in Detectron2 to give the best detection accuracy for these images. In addition, we use a sharpening filter with augmentation techniques. This highlights the profile of the object which helps us recognize it easily. We demonstrate our results by verifying these on TrashCan Data set, both instance and material version. We then explore the best-performing backbone method for this setting. We apply our channel stabilization and augmentation methods to the best-performing technique. We also compare our detection results from Detectron2 using the best backbones with those from Deformable Transformer. The detection result for small size objects in the Instance-version of TRASHCAN 1.0 gives us a 9.53box we get the absolute gain of 7to the baseline.



Publication Date



Channel Stabilization, LAB-Stretching, RetinaNet, Sharpening Filter


IR conditions: non-described