A Systematic Investigation on end-to-end Deep Recognition of Grocery Products in the Wild

Automatic recognition of products on grocery shelf images is a new and attractive topic in computer vision and machine learning since, it can be exploited in different application areas. This paper introduces a complete end-to-end pipeline, without preliminary radiometric and spatial transformations usually involved while dealing with the considered issue, and it provides a systematic investigation of recent machine learning models based on convolutional neural networks for addressing the product recognition task by exploiting the proposed pipeline on a recent challenging grocery product dataset. The investigated models were never been used in this context: they derive from the successful and more generic object recognition task and have been properly tuned to address this specific issue. Besides, also ensembles of nets built by most advanced theoretical fundaments have been taken into account. Gathered classification results were very encouraging since the recognition accuracy has been improved up to 15% with respect to the leading approaches in the state of art on the same dataset. A discussion about the pros and cons of the investigated solutions are discussed by paving the path towards new research lines.