Detection of Data Matrix Encoded Landmarks in Unstructured Environments using Deep Learning

Comparative Analysis of Deep Neural Networks for the Detection and Decoding of Data Matrix Landmarks in Cluttered Indoor Environments

“…Since all models were tuned by using the validation set, this could not be considered as the set to perform the numerical comparisons and assessments. Therefore, a test set was created similarly to the sets conceived in [12]. This set of frames was processed only once by each model and that is why the networks results presented throughout this Section are unbiased and valid.…”

“…However, they all present results for structured environments and in limited situations. In [12], a Faster R-CNN architecture was proposed to detect Data Matrix landmarks in unstructured scenarios. This architecture is quite accurate in detecting objects at multiple scales and outperformed by far the traditional algorithm provided by the libdmtx Python's library in processing time.…”

“…In order to compare and evaluate precisely the networks, they were all trained from scratch. The training and validation sets are the same as those conceived in [12], but at a quarter of the original size of the images (1500 × 2000) because the previous input shape did not correspond to what was going to be used in the final system and hampered the training of low-latency networks like SSD and YOLO. Moreover, data augmentation techniques were performed to increase the training input variance.…”

“…The full dataset was manually labeled in an online toolbox 2 and it is composed of three different subsets: the training set, the validation set, and the test set. The first two were designed in [12], where the training set has 156 frames equally distributed by two different scenarios (a lab and a workshop), and the validation set is also divided into two environments -a hallway (158 frames) and a different workshop (66 frames). Therefore, both training and validation sets were collected in different scenarios to avoid biased results during neural networks training.…”

“…In that context, the work developed in [12] took that challenge of locating visual encoded landmarks (Data Matrix) in the environment using a DNN. That work proved that DL architectures to locate Data Matrix are capable of overcoming the barriers imposed by the traditional techniques, namely accuracy and processing time during the detection task.…”