Proceedings ArticleDOI
An Improved Recognition Method Based on YOLOv3-45k for Refrigerator Item Images
TLDR
In this paper, an improved YOLOv3-45k method was proposed to further improve the recognition effect, which reduced the number of residual modules in feature extraction network to improve the detection speed.Abstract:
Image recognition of refrigerator items has become one of the hotspots in computer vision with the widespread usage of an intelligent refrigerator. There are some problems, such as slow detection speed, missed detection, false detection, when applying a convolutional neural network for the recognition of refrigerator image. To further improve the recognition effect, we proposed an improved YOLOv3-45k method. Firstly, the number of residual modules in feature extraction network is reduced, so that to improve the detection speed. Secondly, the $\mathbf{K}-\mathbf{means}++$ clustering algorithm is used to obtain the anchors of refrigerator items, which is adjusted reasonably by manual method, and then corresponding to their respective feature layers to improve the detection accuracy. The experimental results show that the proposed algorithm can detect 57 images per second on the refrigerator item data set, and the mAP is 84.40%. Compared with the original YOLOv3 algorithm, the proposed YOLOv3-45k method has better detection accuracy and detection speed in the refrigerator item data set.read more
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
Book ChapterDOI
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
Proceedings ArticleDOI
Fast R-CNN
TL;DR: Fast R-CNN as discussed by the authors proposes a Fast Region-based Convolutional Network method for object detection, which employs several innovations to improve training and testing speed while also increasing detection accuracy and achieves a higher mAP on PASCAL VOC 2012.
Proceedings ArticleDOI
YOLO9000: Better, Faster, Stronger
Joseph Redmon,Ali Farhadi +1 more
TL;DR: YOLO9000 as discussed by the authors is a state-of-the-art real-time object detection system that can detect over 9000 object categories in real time using a novel multi-scale training method, offering an easy tradeoff between speed and accuracy.