A Lightweight Feature Fusion Single Shot Multibox Detector for Garbage Detection

Ma, Wen; Wang, Xiao; Yu, Jiong

doi:10.1109/access.2020.3031990

Cited by 46 publications

(16 citation statements)

References 29 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has the advantages of fast speed and strong generalization ability. However, YOLO is insufficient, for shortcomings of YOLO method Ma et al proposed the SSD model in [34], combined with YOLO and the anchor box idea of Faster R-CNN, 6 feature maps of different scales are extracted through the VGG-16 basic network. The fully connected layer is removed so that the detection accuracy is improved.…”

Section: Methods Based On Spectral Algorithmsmentioning

confidence: 99%

Research on Improved Residual Network Classification Method for Defect Recognition of Thermal Battery

Zhang

Bai

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Thermal battery is an ideal power supply for military applications such as artillery and ship equipment. Due to the sheet-type process of the thermal battery, various installation error defects occur in the assembly of thermal battery. Aiming at the problems of low efficiency and low defect-recognition rate of thermal battery detection, a thermal battery defect detection model is proposed based on residual network. First, the squeeze-and-excitation networks (SENet) structure based on the attention mechanism is introduced into residual block of the residual neural network, the connection between the feature extraction channels is established, and the improved deep residual network I-ResNet50 is obtained; Second, in order to prevent overfitting, the defect images processed in the production line and the laboratory are data-enhanced and labeled. Transfer learning strategy is introduced into the recognition model I-ResNet50, and then the training set data samples are input into the recognition model I-ResNet50 for training, and the activation function LReLu and Dropout skills are introduced to improve the classification ability of the I-ResNet50 model; Finally, the recognition model I-ResNet50 is applied to the test set and validation set, and each defect of the thermal battery are output. Comparison experiments are tested under different migration strategies and different optimizers and learning rates, and comparison experiments with the five classic network structures of ResNet50, YOLOV3, MobileNetV2, VGG16, and YOLOV4 are also tested. The test data show that the recognition accuracy rates of qualified images and the three types of defective images (Qualified Assembly, Missing Current Plate, Wrong Number of Stacks, and Reverse Stack) can reach 99.64%, 98.17%, 99.11%, and 95.40%, respectively, the overall recognition accuracy rate can reach 98.10%. The test results illustrate the model can detect thermal battery defects more accurately and quickly, and has good defect diagnosis ability, which is nearly 5% higher than the traditional method, and a new solution for defect detection in practical industrial scenarios of thermal battery is provided.

show abstract

Section: Methods Based On Spectral Algorithmsmentioning

confidence: 99%

Research on Improved Residual Network Classification Method for Defect Recognition of Thermal Battery

Zhang

Bai

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The SSD series has been adopted in waste classification in recent years. − Successful applications are presented in Table . The SSD series was competent to classify most kinds of domestic wastes at fairly high mAP with high detection speed, except for DSSD, which only detected targets at 2 fps .…”

Section: Development and Status Quo Of Sensor-based Waste Sorting Tec...mentioning

confidence: 99%

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

Zhao

2022

Environ. Sci. Technol.

View full text Add to dashboard Cite

The increase in global population and improvement of living standards have stirred up a continuous increase in solid waste generation, while simple incineration and landfilling bring about serious environmental and health concerns. In order to improve resource recovery and mitigate pollution, noncontacting and nondestructive sensor-based waste sorting systems are applied to enhance solid waste classification. In recent years, in addition to the rapid development of computer hardware, especially improvements of GPU computing capacity, complicated and efficient classification algorithms have emerged and been widely used in industrial sectors. These advances allow computers to process signals from sensors more quickly and accurately and to classify matters automatically. This article introduces widely applied sensor-based technologies in solid waste sorting and analyzes applicable conditions for each specific method. The latest developed algorithms are critically compared with competitive counterparts. Successful practices are described, and findings are highlighted. Though spectroscopic-based and vision-based waste classifications have achieved high performance in accuracy and detection speed, challenges and future directions can still provide wide development opportunities. Concretely, these opportunities generally comprise classification of indistinct plastics, application of the latest object detection algorithms, appropriate data set formulating, and sensor combination for multiple sorting tasks within a single system.

show abstract

“…The improved YOLOv2 network model is used in the system's front-end embedded module for garbage detection and recognition. As a result of low image resolution, a research by [58] a new and innovative feature fusion module that is lightweight was proposed as part of an algorithm that is an improved single-shot multibox detector (SSD). In the course of this study, the backbone network of VGG16 was upgraded to ResNet-101 in order to accomplish more precise detection.…”

Section: A Waste Detectionmentioning

confidence: 99%

A Survey on Waste Detection and Classification Using Deep Learning

Abdu

Noor

2022

IEEE Access

View full text Add to dashboard Cite

Waste or trash management is receiving increased attention for intelligent and sustainable development, particularly in developed and developing countries. The waste or trash management system comprises several related processes that carry out various complex functions. Recently, interest in deep learning (DL) has increased in providing alternative computational techniques for determining the solution to various waste or trash management problems. Researchers have concentrated on this domain, and as a result, significant research has been published, particularly in recent years. According to the literature, a few comprehensive surveys have been done on waste detection and classification. However, no study has investigated the application of DL to solve waste or trash management problems in various domains and highlight the available datasets for waste detection and classification in different domains. To this end, this survey contributes by reviewing various image classification and object detection models, and their applications in waste detection and classification problems, providing an analysis of waste detection and classification techniques with precise and organized representation and compiling over twenty benchmarked trash datasets. Also, we backed up the study with the challenges of existing methods and the future potential in this field. This will give researchers in this area a solid background and knowledge of the state-of-the-art deep learning models and insight into the research areas that can still be explored.

show abstract

A Lightweight Feature Fusion Single Shot Multibox Detector for Garbage Detection

Cited by 46 publications

References 29 publications

Research on Improved Residual Network Classification Method for Defect Recognition of Thermal Battery

Research on Improved Residual Network Classification Method for Defect Recognition of Thermal Battery

Sensor-Based Technologies in Effective Solid Waste Sorting: Successful Applications, Sensor Combination, and Future Directions

A Survey on Waste Detection and Classification Using Deep Learning

Contact Info

Product

Resources

About