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- Occluded prohibited object detection in X-ray images with . . .
Proposals were sent to the feedforward neural network for prohibited object detection Experimental results show that the framework could effectively solve the unbalance problem between foreground and background in X-ray images And thus, the detection accuracy of occluded objects has been improved
- Feature extraction of detected segmentated objects #3957
From your question, it sounds like you want to perform object detection using YOLOv8 and then extract the learned features of those detected objects (in your case, cars) for further processing such as matching This is a common scenario in many applications including yours
- Handling occlusion in prohibited item detection from X-ray . . .
Prohibited item detection from X-ray images can automatically search for prohibited items in passenger packages, thereby effectively suppressing terrorism and criminal incidents
- PIXDet: Prohibited Item Detection in X-Ray Image Based on . . .
In this article, we propose the novel ideologies of whole-process feature fusion and local–global semantic dependency interaction to improve the automatic detection of prohibited items
- STRay: A Model for Prohibited Item Detection in Security . . .
Abstract—Addressing issues such as mutual occlusion of items and small scale of prohibited items in X-ray security in-spection image detection, we propose an improved X-ray contra-band detection model based on YOLOv7 named STRay
- EM-YOLO: An X-ray Prohibited-Item-Detection Method Based on . . .
To better detect prohibited items in security X-ray images with these characteristics, we propose EM-YOLOv7, which is composed of both an edge feature extractor (EFE) and a material feature extractor (MFE) We used the Soft-WIoU NMS method to solve the problem of object overlap
- Lightweight prohibited items detection model in X-ray images . . .
Consequently, an enhanced YOLOv7-tiny model is presented in this research and used to the problem of forbidden object recognition in X-ray images First, the upgraded model’s feature extraction backbone is a lightweight network called FasterNet, which lowers computing complexity and speeds up detection
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