Research of garbage salvage system based on deep learning

Yang, Wang; Che, Jin; zhang, lijia; ma, mingfa

doi:10.1117/12.2637385

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…TF40 is an millimeter-level accuracy LiDAR with a range of up to 40 m [ 36 , 37 , 38 ]. Accuracy in mm is essential because the trash object is relatively small, 15 cm × 20 cm.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Trash Identification on the House Compound Using a Convolutional Neural Network (CNN) and Sensor System

Naf’an

Sulaiman

Ali

2023

Sensors

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This study aims to optimize the object identification process, especially identifying trash in the house compound. Most object identification methods cannot distinguish whether the object is a real image (3D) or a photographic image on paper (2D). This is a problem if the detected object is moved from one place to another. If the object is 2D, the robot gripper only clamps empty objects. In this study, the Sequential_Camera_LiDAR (SCL) method is proposed. This method combines a Convolutional Neural Network (CNN) with LiDAR (Light Detection and Ranging), with an accuracy of ±2 mm. After testing 11 types of trash on four CNN architectures (AlexNet, VGG16, GoogleNet, and ResNet18), the accuracy results are 80.5%, 95.6%, 98.3%, and 97.5%. This result is perfect for object identification. However, it needs to be optimized using a LiDAR sensor to determine the object in 3D or 2D. Trash will be ignored if the fast scanning process with the LiDAR sensor detects non-real (2D) trash. If Real (3D), the trash object will be scanned in detail to determine the robot gripper position in lifting the trash object. The time efficiency generated by fast scanning is between 13.33% to 59.26% depending on the object's size. The larger the object, the greater the time efficiency. In conclusion, optimization using the combination of a CNN and a LiDAR sensor can identify trash objects correctly and determine whether the object is real (3D) or not (2D), so a decision may be made to move the trash object from the detection location.

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“…TF40 is an millimeter-level accuracy LiDAR with a range of up to 40 m [ 36 , 37 , 38 ]. Accuracy in mm is essential because the trash object is relatively small, 15 cm × 20 cm.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Trash Identification on the House Compound Using a Convolutional Neural Network (CNN) and Sensor System

Naf’an

Sulaiman

Ali

2023

Sensors

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“…The experimental results showed that YOLOv3 achieved higher detection accuracy compared to the other four algorithms. Yang et al [17] designed a garbage collection system with autonomous cruising, recognition, and detection capabilities, equipped with the ROS robot operating system and utilizing the YOLO v4 object detection model to detect garbage, which was then applied to an intelligent garbage collection system using the detected targets and their location information. Kong et al [18] proposed an intelligent robot system, IWSCR, for water surface cleaning, where the core component of the visual system was designed based on YOLOv3.…”

Section: Water Surface Garbage Detectionmentioning

confidence: 99%

Strong and Weak Supervision Combined with CLIP for Water Surface Garbage Detection

Ma,

Chu,

Liu

et al. 2023

Water

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Water surface garbage has a significant impact on the protection of water environments and ecological balance, making water surface garbage object detection a critical task. Traditional supervised object detection methods require a large amount of annotated data. To address this issue, we propose a method that combines strong and weak supervision with CLIP (Contrastive Language–Image Pretraining) for water surface garbage object detection. First, we train on a dataset annotated with strong supervision, using traditional object detection algorithms to learn the location information of water surface garbage. Then, we input the water surface garbage images into CLIP’s visual encoder to obtain visual feature representations. Simultaneously, we train CLIP’s text encoder using textual description annotations to obtain textual feature representations of the images. By fusing the visual and textual features, we obtain comprehensive feature representations. In the weak supervision training phase, we input the comprehensive feature representations into the object detection model and employ a training strategy that combines strong and weak supervision to detect and localize water surface garbage. To further improve the model’s performance, we introduce attention mechanisms and data augmentation techniques to enhance the model’s focus and robustness towards water surface garbage. By conducting experiments on two water surface garbage datasets, we validate the effectiveness of the proposed method based on the combination of strong and weak supervision with CLIP for water surface garbage object detection tasks.

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Research of garbage salvage system based on deep learning

Cited by 2 publications

References 4 publications

Optimization of Trash Identification on the House Compound Using a Convolutional Neural Network (CNN) and Sensor System

Optimization of Trash Identification on the House Compound Using a Convolutional Neural Network (CNN) and Sensor System

Strong and Weak Supervision Combined with CLIP for Water Surface Garbage Detection

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