EWasteNet: A Two-Stream Data Efficient Image Transformer Approach for E-Waste Classification

Islam, Niful; Hasan Jony, Md. Mehedi; Hasan, Emam; Sutradhar, Sunny; Rahman, Atikur; Islam, Md. Motaharul

doi:10.1109/icsecs58457.2023.10256321

Cited by 4 publications

(1 citation statement)

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“…They used the Swin transformer to establish a model of the relationship between foreign objects and the background, while utilizing dense connections to enhance the re-use of local features, thus avoiding over-fitting. In [44], a new application of a transformer in an electronic waste detection method was provided. It provided an efficient image conversion model EWasteNet based on a transformer for accurate classification of electronic waste using dual stream data.…”

Section: Transformer For Object Detectionmentioning

confidence: 99%

ATS-YOLOv7: A Real-Time Multi-Scale Object Detection Method for UAV Aerial Images Based on Improved YOLOv7

Zhang,

Shao,

et al. 2023

Electronics

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The objects in UAV aerial images have multiple scales, dense distribution, and occlusion, posing considerable challenges for object detection. In order to address this problem, this paper proposes a real-time multi-scale object detection method based on an improved YOLOv7 model (ATS-YOLOv7) for UAV aerial images. First, this paper introduces a feature pyramid network, AF-FPN, which is composed of an adaptive attention module (AAM) and a feature enhancement module (FEM). AF-FPN reduces the loss of deep feature information due to the reduction of feature channels in the convolution process through the AAM and FEM, strengthens the feature perception ability, and improves the detection speed and accuracy for multi-scale objects. Second, we add a prediction head based on a transformer encoder block on the basis of the three-head structure of YOLOv7, improving the ability of the model to capture global information and feature expression, thus achieving efficient detection of objects with tiny scales and dense occlusion. Moreover, as the location loss function of YOLOv7, CIoU (complete intersection over union), cannot facilitate the regression of the prediction box angle to the ground truth box—resulting in a slow convergence rate during model training—this paper proposes a loss function with angle regression, SIoU (soft intersection over union), in order to accelerate the convergence rate during model training. Finally, a series of comparative experiments are carried out on the DIOR dataset. The results indicate that ATS-YOLOv7 has the best detection accuracy (mAP of 87%) and meets the real-time requirements of image processing (detection speed of 94.2 FPS).

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Section: Transformer For Object Detectionmentioning

confidence: 99%