# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 5 Product Detection and Recognition
### Product Detection Based on Deep Learning  
Grocery product detection is intended to locate items in a grocery store without categorisation. Detecting a few large objects in an image is easier than detecting grocery products in dense racks since there are many small objects. ScaleNet [81] used the ResNet feature extractor and reduced the searching space of scales by estimating the object scale and using it to guide object proposal generation in supermarket images. The object proposal detection is based on the SharpMask method, which requires annotation at pixel level. [PERSON] et al. [3] presented an alternative strategy aiming to overcome the challenge posed by common multiple overlapping bounding boxes in densely packed scenes. The CNN detector estimates the bounding box, the objectness and the Soft-IoU score as a measure of detection quality. The Expectation-Maximization (EM) unit uses the previous score and detections to select the correct location. The model was evaluated in the SKU-110K dataset and showed that even the best results were still significantly saturated. [PERSON] et al. [82] introduced an R-CNN detector, replacing the greedy non-maximal suppression with a novel graph-based non-maximal suppression that obtained the best proposal region by combining classification scores and the product classes of the overlapping region proposals. In [50], the authors proposed a retail product detection based on DenseBox-style [83] with a Feature Pyramid Network (FFN). The authors re-defined the centre of quadrilateral bounding boxes computing Quad-Centerness (QC) and introduced the Soft-Scale (SS) algorithm to detect objects of arbitrary shape. The corner refinement module was added to improve heatmap prediction and the location coordinates. The Quad-Centerness (QC), Soft-Scale (SS) and corner refinement module (CRM) gradually improved the mean average precision (mAP). To reduce the need to collect a large amount of data, a one-stage one-shot detector--OS2D--was proposed in [84]. It joined the localization and recognition phases, applying a fully convolutional TransformNet to extract an image's features. A dense correlation and a feed-forward geometric transformation model were used to match and align features. The bilinear resample served to finalise the training. The assessment was performed using the GroZi-3.2k dataset [45] and the authors concluded that the OS2D model outperformed some reliable baselines.  
A fair comparison between these detection models is hard, since the methods were assessed using different test datasets and distinct metrics. Table 4 demonstrates the specific approach implemented in each method.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **Annotations** & **Dataset** & **Scores** & **Metrics** \\ \hline ScaleNet [81] & Mask based/segmentation & MS COCO & 0.578 & AR@1k \\ \hline [PERSON] et al. [3] & Bounding box & SKU-110K & 0.492 & AP \\ \hline \multirow{3}{*}{[PERSON] et al. [82]} & \multirow{3}{*}{Bounding box} & GroZi-3.2 & 0.802 & \multirow{3}{*}{F1} \\  & & WebMarket & 0.755 & \\ \cline{1-1}  & & GroZi-120 & 0.448 & \\ \hline \multirow{3}{*}{RetailDet [50]} & \multirow{3}{*}{Bounding box} & SKU110k & 0.590 & \multirow{3}{*}{mAP} \\  & & UntiailDet & OD:0.587, CD:0.509 & \\ \hline OS2D [84] & Bounding box & GroZi-3.2k & 0.850 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of grocery product detection methods. In the RetailDet, the evaluation was made in two test sets, the origin-domain, which is represented as OD, and cross-domain as CD.