A multi-label waste detection model based on transfer learning

Zhang, Qiang; Yang, Qifan; Zhang, Xujuan; Wei, Wei; Bao, Qi; Su, Jinqi; Liu, Xueyan

doi:10.1016/j.resconrec.2022.106235

Cited by 47 publications

(16 citation statements)

References 35 publications

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“…Some applications are shown in Table . The YOLO series could achieve satisfying performance. ,,− Moreover, practices manifested a startling detection speed for YOLO, which always achieved real-time. For example, YOLOv4 reached a speed 24% higher than faster R-CNN, while the mAP was 2.03% higher .…”

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

confidence: 99%

“…The YOLO series could achieve satisfying performance. ,,− Moreover, practices manifested a startling detection speed for YOLO, which always achieved real-time. For example, YOLOv4 reached a speed 24% higher than faster R-CNN, while the mAP was 2.03% higher . Therefore, the YOLO series has as good a performance as the R-CNN series, and high detection speed qualified the YOLO series to adapt operational requirements.…”

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

confidence: 99%

“…Vo et al constructed a VGG network and a long softmax, transferred the model from the TrashNet to VN-Trash data set, and got a 94% mAP . Zhang et al pretrained YOLOv4 in the PASCAL VOC data set, then fine-tuned parameters through the MULTI-TRASH data set, and obtained a mAP of 92.23% …”

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

confidence: 99%

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

Zhao

2022

Environ. Sci. Technol.

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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.

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

confidence: 99%

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

confidence: 99%

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

Zhao

2022

Environ. Sci. Technol.

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“…Dalam pengelolaan limbah produksi, pemanfaatan AI juga memiliki potensi yang sangat besar. Berdasarkan beberapa penelitian terdahulu, AI, khususnya deep learning, memiliki peran yang signifikan pada proses pemisahan sampah [11]- [13]. Sebagai contoh, penelitian yang dilakukan oleh Dang dkk yang menciptakan arsitektur untuk mengenali jenis limbah logam.…”

Section: Pendahuluanunclassified

Implementasi Artificial Intelligence Pada Pengembangan Sistem Deteksi Limbah Industri Produk Kulit Otomatis

Frannita

Putri

Hidayahtullah

2022

bptkspk

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Rapid globalization has led to the growth of a significant amount of waste, including in the industry. Due to the considerable amount of growth of waste every year, effective and efficient waste management is needed to protect our environment. In the leather product industry, waste management is strongly important since it may have a significant impact to the employee and production process. Regarding those issues, waste management technology is considered proposed in order to solve those problems. Current research reported the outstanding work of implementing artificial intelligence for detecting and recognizing industrial waste. Artificial intelligence was proven to be a highly recommended approach that is able to classify several waste types with outstanding performance. Regardless of those facts, artificial intelligence still remains several hurdles, such as the high computational demands, especially for deep learning networks. Regarding the mentioned issue, we proposed a more proper deep learning network for recognizing industrial waste. In this research work, we use Single Shot Detector (SSD) to recognize and classify industrial waste. Our proposed solution was performed in the TrashNet dataset and Waste Picture dataset. Our proposed solution successfully achieve mAP of 0.8813, accuracy of 0,9795, precision of 0,9985 and recall of 0,9693 in the training process. Whereas, in the testing process we achieve average accuracy of 0,8254. According to those results, we can conclude that our proposed solution is suitable for industrial waste detection and has potential to be implemented as an embedded system for recognizing industrial waste automatically.

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“…Mao et al [ 5 ] developed a domestic waste dataset based on the Taiwan region for YOLOv3 training and achieved an accuracy of 92.12%. Zhang et al [ 6 ] proposed a YOLO_WASTE waste classification model, which was trained using migration learning based on the YOLOv4 network and achieved excellent results on a self-built dataset.…”

Section: Introductionmentioning

confidence: 99%

Waste Detection System Based on Data Augmentation and YOLO_EC

Fan

Cui

Fei

2023

Sensors

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The problem of waste classification has been a major concern for both the government and society, and whether waste can be effectively classified will affect the sustainable development of human society. To perform fast and efficient detection of waste targets in the sorting process, this paper proposes a data augmentation + YOLO_EC waste detection system. First of all, because of the current shortage of multi-objective waste classification datasets, the heavy workload of human data collection, and the limited improvement of data features by traditional data augmentation methods, DCGAN (deep convolution generative adversarial networks) was optimized by improving the loss function, and an image-generation model was established to realize the generation of multi-objective waste images; secondly, with YOLOv4 (You Only Look Once version 4) as the basic model, EfficientNet is used as the backbone feature extraction network to realize the light weight of the algorithm, and at the same time, the CA (coordinate attention) attention mechanism is introduced to reconstruct the MBConv module to filter out high-quality information and enhance the feature extraction ability of the model. Experimental results show that on the HPU_WASTE dataset, the proposed model outperforms other models in both data augmentation and waste detection.

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A multi-label waste detection model based on transfer learning

Cited by 47 publications

References 35 publications

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

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

Implementasi Artificial Intelligence Pada Pengembangan Sistem Deteksi Limbah Industri Produk Kulit Otomatis

Waste Detection System Based on Data Augmentation and YOLO_EC

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