In LiDAR sensing, glass, mirrors and other materials often cause inconsistent data readings from reflections. This causes problems in robotics and 3D reconstruction, especially with respect to localization, mapping and, thus, navigation. Extending our previous work, we construct a global, optimized map of reflective planes, in order to then classify all LiDAR readings at the end. For this, we optimize the reflective plane parameters of the plane detection of multiple scans. In a further method, we apply the reflective plane estimation in a plane SLAM algorithm, highlighting the applicability of our method for robotics. As our experiments will show, this approach provides superior classification accuracy compared to the single scan approach. The code and data for this work are available as open source online.

OBJECTIVE: Specular reflections in endoscopic images not only disturb visual perception but also hamper computer vision algorithm performance. However, the intricate nature and variability of these reflections, coupled with a lack of relevant datasets, pose ongoing challenges for removal.
METHODS: We present EndoSRR, a robust method for eliminating specular reflections in endoscopic images. EndoSRR comprises two stages: reflection detection and reflection region inpainting. In the reflection detection stage, we adapt and fine-tune the segment anything model (SAM) using a weakly labeled dataset, achieving an accurate reflection mask. For reflective region inpainting, we employ LaMa, a fast Fourier convolution-based model trained on a 4.5M-image dataset, enabling effective inpainting of arbitrarily shaped reflection regions. Lastly, we introduce an iterative optimization strategy for dual pre-trained models to refine the results of specular reflection removal, named DPMIO.
RESULTS: Utilizing the SCARED-2019 dataset, our approach surpasses state-of-the-art methods in both qualitative and quantitative evaluations. Qualitatively, our method excels in accurately detecting reflective regions, yielding more natural and realistic inpainting results. Quantitatively, our method demonstrates superior performance in both segmentation evaluation metrics (IoU, E-measure, etc.) and image inpainting evaluation metrics (PSNR, SSIM, etc.).
CONCLUSIONS: The experimental results underscore the significance of proficient endoscopic specular reflection removal for enhancing visual perception and downstream tasks. The methodology and results presented in this study are poised to catalyze advancements in specular reflection removal, thereby augmenting the accuracy and safety of minimally invasive surgery.

Reflective phenomena often occur in the detecting process of pointer meters by inspection robots in complex environments, which can cause the failure of pointer meter readings. In this paper, an improved k-means clustering method for adaptive detection of pointer meter reflective areas and a robot pose control strategy to remove reflective areas are proposed based on deep learning. It mainly includes three steps: (1) YOLOv5s (You Only Look Once v5-small) deep learning network is used for real-time detection of pointer meters. The detected reflective pointer meters are preprocessed by using a perspective transformation. Then, the detection results and deep learning algorithm are combined with the perspective transformation. (2) Based on YUV (luminance-bandwidth-chrominance) color spatial information of collected pointer meter images, the fitting curve of the brightness component histogram and its peak and valley information is obtained. Then, the k-means algorithm is improved based on this information to adaptively determine its optimal clustering number and its initial clustering center. In addition, the reflection detection of pointer meter images is carried out based on the improved k-means clustering algorithm. (3) The robot pose control strategy, including its moving direction and distance, can be determined to eliminate the reflective areas. Finally, an inspection robot detection platform is built for experimental study on the performance of the proposed detection method. Experimental results show that the proposed method not only has good detection accuracy that achieves 0.809 but also has the shortest detection time, which is only 0.6392 s compared with other methods available in the literature. The main contribution of this paper is to provide a theoretical and technical reference to avoid circumferential reflection for inspection robots. It can adaptively and accurately detect reflective areas of pointer meters and can quickly remove them by controlling the movement of inspection robots. The proposed detection method has the potential application to realize real-time reflection detection and recognition of pointer meters for inspection robots in complex environments.