# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 5 Product Detection and Recognition
### End-to-End Product Classification Based on Deep Learning  
Object detection and object recognition can be combined to create effective end-to-end product identification. To overcome the obstacles placed by the scarcity of relevant data, [PERSON] et al. [94] proposed a classification model with just one training example per class. The initial detection and classification were performed based on a non-parametric probabilistic model trained on the limited data. In the second phase, a CNN-based model used the previous information to produce fine-grained classification refinements. Synthetic data were created from a few training examples to train the CNN model and the final output combines the scores of the first model with the corresponding CNN confidences. A coarse-to-fine approach was proposed in [44] with product regions detected by recurringfeatures and classified into coarse classes. For a fine-grained classification, they employed attention maps built by SIFT features to guide the CNN classifier to focus on fine discriminative details. By using one-shot learning, the identification is based on feature matching. The system could adjust to new product classes without having to retrain the classifier. In [95], the global feature description of the detected region was compared to a database of descriptions of iconic product images. YOLOv5 was used to extract the proposal regions and naive Bayes (NB) similarity search provided the product classification. The recognition results were improved with a combination of shape, size and colour features encoded by Fisher vectors and the Dirichlet function. The mAP of the proposed model achieved 58% on GroZi-120 dataset. Using a one-shot learning approach, [4] located objects on the shelves with a CNN-based detector, such as YOLOv2 [96]. The global product descriptors of reference and query images are obtained by computing MAC (maximum activations of convolutions) features from the CNN _embedder_ pre-trained on ImageNet to produce embedding vectors. The recognition was obtained by pairwise means K-NN similarity search. As the training and query image domain are different, later in [97], the same authors incorporated GAN with adversarial behaviour in the _embedder_ to deal with domain shift, to augment the training set size and for the CNN to learn a stronger embedding function. [PERSON] et al. [98] refined the framework suggested in [4]. The YOLO-based detector was replaced by a Faster RCNN integrated with a Feature Pyramid Network (FPN) to enhance multi-scale object detection. Intending to deploy the model on an edge device with little memory space, they used a lightweight ResNet-18-based product recognition model instead of the more complex VGG-16 model. This framework was trained over a large set of images created by augmentation techniques. They were able to introduce a time-efficient framework. Table 6 summarizes the quantitative performance of grocery detection and recognition methods on the GroZi-3.2K dataset. Since the model proposed in [95] was just evaluated on the SKU-110K dataset, their results are shown to provide a guideline.