Dirt Detection Research Articles

Solar Panel Dirt Detection and Cleaning System

The proposed solar panel dirt detection and cleaning system improves the efficiency in generating output voltage that integrates Arduino micro-controller with voltage recognition technology to enhance the efficiency of solar energy harvesting. The system utilizes voltage divider circuit to identify dirt or debris on the solar panel surface by analyzing changes in output voltage produced by the solar panel. Along with voltage divider circuit, LDR (Light Dependent Resistor) sensor is utilized to enable the cleaning process only in day time when the intensity level of the LDR is more than threshold value. Upon detection, the Arduino micro-controller triggers a cleaning mechanism to remove the identified dirt. The cleaning mechanism can be designed using actuators, brushes and other appropriate tools. The Arduino's programmability allows for customization of cleaning intervals, ensuring timely maintenance.

Solar panel surface dirt detection and removal based on arduino color recognition

Color sensing is a technique for identifying physical changes in materials based on appearance assessment. Dirt deposition on solar panels can change their physical appearance and performance. Considering that dirt accumulation on solar panels needs monitoring to make efficient cleaning schedules, reduce unnecessary costs, and optimize solar panel output generation. Color sensing can achieve fast, accurate, and economical dirt detection, unlike the use of robotic cameras, mathematical formulae, and considering varying output current and voltage methods. Here, we introduce a method that detects and removes dirt on solar panels based on TCS3200 and Arduino Uno components. The approach targets (i.) Panel color measurement, calibration, threshold selection process, (ii.) comparison of color measurement values, and (iii.) align further calibration in response to discoloration of solar panels. This method aims to correct the dirt detection methods previously in use. Hence, a high-speed rolling brush arrangement is designed to improve the cleaning of the solar panel without using water. Further investigations of the panel's color may require some improvement in terms of increasing the sensitivity of the color sensor even with increased distance from the solar panel. Combining multiple color sensors may also be necessary.

Pantograph Detection Algorithm with Complex Background and External Disturbances.

As an important equipment for high-speed railway (HSR) to obtain electric power from outside, the state of the pantograph will directly affect the operation safety of HSR. In order to solve the problems that the current pantograph detection method is easily affected by the environment, cannot effectively deal with the interference of external scenes, has a low accuracy rate and can hardly meet the actual operation requirements of HSR, this study proposes a pantograph detection algorithm. The algorithm mainly includes three parts: the first is to use you only look once (YOLO) V4 to detect and locate the pantograph region in real-time; the second is the blur and dirt detection algorithm for the external interference directly affecting the high-speed camera (HSC), which leads to the pantograph not being detected; the last is the complex background detection algorithm for the external complex scene "overlapping" with the pantograph when imaging, which leads to the pantograph not being recognized effectively. The dirt and blur detection algorithm combined with blob detection and improved Brenner method can accurately evaluate the dirt or blur of HSC, and the complex background detection algorithm based on grayscale and vertical projection can greatly reduce the external scene interference during HSR operation. The algorithm proposed in this study was analyzed and studied on a large number of video samples of HSR operation, and the precision on three different test samples reached 99.92%, 99.90% and 99.98%, respectively. Experimental results show that the algorithm proposed in this study has strong environmental adaptability and can effectively overcome the effects of complex background and external interference on pantograph detection, and has high practical application value.

A Deep Learning-Based Dirt Detection Computer Vision System for Floor-Cleaning Robots with Improved Data Collection

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application.

Influence of the filter cloth and nozzles type on the in-situ cleaning procedure of filter presses

Exemplary representation of the contaminated surface and the resulting binary image with coordinate generation based on the effective radius of the cleaning nozzle and the shortest way of cleaning procedure (left to right). • The surface contamination is lower with smaller meshes within the same weave type. • Calendering of the fabric surface does not lead to less surface contamination. • Multifilament fabrics have the lowest surface contamination with the smallest amount of cleaning agent. • Mathematical description of the cleaning process by a regressive model possible. • Full jet nozzle to be preferred for surface cleaning. • Optimization of the dirt detection by variation with colour filters and light sources possible.

A method for inspecting the cleanliness of spinneret holes

At present, computer vision system is widely used for the cleanliness inspection for spinneret holes, but it has a high misjudgment rate for many holes with small dirt. In this paper, a method is proposed to improve the accuracy of the cleanliness inspection. The method has four sequential phases. First, the closed contour curve of a standard hole and its curvature are extracted. Based on double-threshold segmentation of the contour curve, line and arc segments are segmented to generate a closed piecewise curve model. Second, the model is fitted to the closed contour curve L1 of the hole to be inspected based on a nonlinear least squares principle, and the distance curve that represents the shortest distance between the closed piecewise curve L2 constructed from the aligned model and L1 is calculated based on the nearest neighbor search algorithm. Third, the dirt detection curve L3 is generated from the distance curve weighted by a blended weighting curve. Final, based on a global threshold combining with unevenness elimination of L3, the cleanliness index is calculated based on the segmentation and location of dirt, and is used to judge whether the hole is qualified or not. The experimental results of four databases demonstrate that, the proposed method provides better performance compared with the traditional method.

Vision-Based Dirt Detection and Adaptive Tiling Scheme for Selective Area Coverage

This paper proposes a visual dirt detection algorithm and a novel adaptive tiling-based selective dirt area coverage scheme for reconfigurable morphology robot. The visual dirt detection technique utilizes a three-layer filtering framework which includes a periodic pattern detection filter, edge detection, and noise filtering to effectively detect and segment out the dirt area from the complex floor backgrounds. Then adaptive tiling-based area coverage scheme has been employed to generate the tetromino morphology to cover the segmented dirt area. The proposed algorithms have been validated in Matlab environment with real captured dirt images and simulated tetrominoes tile set. Experimental results show that the proposed three-stage filtering significantly enhances the dirt detection ratio in the incoming images with complex floors with different backgrounds. Further, the selective dirt area coverage is performed by excluding the already cleaned area from the unclean area on the floor map by incorporating the tiling pattern generated by adaptive tetromino tiling algorithm.

Optical dirt detection for a demand-controlled cleaning of woven filter cloth

Abstract To cope with ever rising quality demands while in constant transition to flexible product lines, cleaning should be considered an integral part of any process design and processing operation. For many unit operations feasibility has been proven. However, in the cross-sectional technology of solid-liquid separation, progress in hygienic design of filtration equipment did not keep pace. As an answer we propose a closed-loop control for cleaning intensity and present a method to evaluate the cleanliness of any woven filter cloth. The weave’s periodicity is evaluated automatically, based on a reported algorithm. By means of image analysis, particle deposits are detected as irregularities on the near-regular weave pattern. Employing background subtraction, we do not rely on overall contrasts in color or brightness between cloth and soiling. If there is a contrast, we reliably find single particles (of 200 µm in our example), as we demonstrate on different color channels of RGB images. The proposed algorithm was implemented in Matlab and executed on a desktop PC in on-line speed. It will enable economical, safe and reliable cleaning of belt filters and filter presses.

Non-motion-compensated region-based dirt detection for film archive restoration

A novel non-motion-compensated method is proposed for dirt detection in archived film sequences. A confidence measurement extracted from raw differences between current frame and each of the previous and next frames is used to exploit the temporally impulsive nature of dirt impairments. Further evolution of the confidence signal enables the minimization of false alarms and the fine-tuning of detector sensitivity. After morphological filtering and consistency checks candidate regions of dirt emerge, enabling the computation of binary detection masks. Our experiments show that our method compares favorably with extended spike detection index (SDIp), rank order detector (ROD), and lower-upper-middle (LUM) approaches and provides efficient and robust detection performance for a wide range of archived film material.

Efficient detection of temporally impulsive dirt impairments in archived films

We propose a novel approach for the detection of temporally impulsive dirt impairments in archived film sequences. Our method does not require motion compensation and uses raw differences between the current frame and each of the previous and next frames to extract a confidence signal which is used to localize and label dirt regions. A key feature of our method is the removal of false alarms by local region-growing. Unlike other work utilizing manually added dirt impairments, we test our method on real film sequences with objective ground truth obtained by infrared scanning. With confidence information extracted from color channels, dirt areas of low contrast to the corresponding gray image can be successfully detected by our method when motion-based methods fail. Comparisons with established algorithms demonstrate that our method offers more efficient, robust and accurate dirt detection with fewer false alarms for a wide range of test material.

Dirt detection on brown eggs by means of color computer vision

In the last 20 yr, different methods for detecting defects in eggs were developed. Until now, no satisfying technique existed to sort and quantify dirt on eggshells. The work presented here focuses on the design of an off-line computer vision system to differentiate and quantify the presence of different dirt stains on brown eggs: dark (feces), white (uric acid), blood, and yolk stains. A system that provides uniform light exposure around the egg was designed. In this uniform light, pictures of dirty and clean eggs were taken, stored, and analyzed. The classification was based on a few standard logical operators, allowing for a quick implementation in an online set-up. In an experiment, 100 clean and 100 dirty eggs were used to validate the classification algorithm. The designed vision system showed an accuracy of 99% for the detection of dirt stains. Two percent of the clean eggs had a light-colored eggshell and were subsequently mistaken for showing large white stains. The accuracy of differentiation of the different kinds of dirt stains was 91%. Of the eggs with dark stains, 10.81% were mistaken for having bloodstains, and 33.33% of eggs with bloodstains were mistaken for having dark stains. The developed system is possibly a first step toward an on line dirt evaluation technique for brown eggs.

全自動きょう雑物測定システムＤＣＷ パルプのオンラインきょう雑物測定システム

DCW is fully automatic system for the measurement of dirt count characteristics of recycled and chemical pulps. DCW measurement is based on image analysis with a CCD-camera. Dirt is recognized using contrast : a dirts is a darker area in the sample comparing to the surrounding pulp.DCW is based on PulpExpert dirt detection technology, which has been widely used in PulpExpert Pulp Quality Analyzer's DirtCount Unit, Model, which are devices for measurement of dirt count from pulp sheets.