Sign language recognition based on deep learning and low-cost handcrafted descriptors

In recent years, deep learning techniques have been used to develop sign language recognition systems, potentially serving as a communication tool for millions of hearing-impaired individuals worldwide. However, there are inherent challenges in creating such systems. Firstly, it is important to consider as many linguistic parameters as possible in gesture execution to avoid ambiguity between words. Moreover, to facilitate the real-world adoption of the created solution, it is essential to ensure that the chosen technology is realistic, avoiding expensive, intrusive, or low-mobility sensors, as well as very complex deep learning architectures that impose high computational requirements. Based on this, our work aims to propose an efficient sign language recognition system that utilizes low-cost sensors and techniques. To this end, an object detection model was trained specifically for detecting the interpreter's face and hands, ensuring focus on the most relevant regions of the image and generating inputs with higher semantic value for the classifier. Additionally, we introduced a novel approach to obtain features representing hand location and movement by leveraging spatial information derived from centroid positions of bounding boxes, thereby enhancing sign discrimination. The results demonstrate the efficiency of our handcrafted features, increasing accuracy by 7.96% on the AUTSL dataset, while adding fewer than 700 thousand parameters and incurring less than 10 milliseconds of additional inference time. These findings highlight the potential of our technique to strike a favorable balance between computational cost and accuracy, making it a promising approach for practical sign language recognition applications.

Artificial intelligence for detection and quantification of rust and leaf miner in coffee crop

Pest and disease control plays a key role in agriculture since the damage caused by these agents are responsible for a huge economic loss every year. Based on this assumption, we create an algorithm capable of detecting rust (Hemileia vastatrix) and leaf miner (Leucoptera coffeella) in coffee leaves (Coffea arabica) and quantify disease severity using a mobile application as a high-level interface for the model inferences. We used different convolutional neural network architectures to create the object detector, besides the OpenCV library, k-means, and three treatments: the RGB and value to quantification, and the AFSoft software, in addition to the analysis of variance, where we compare the three methods. The results show an average precision of 81,5% in the detection and that there was no significant statistical difference between treatments to quantify the severity of coffee leaves, proposing a computationally less costly method. The application, together with the trained model, can detect the pest and disease over different image conditions and infection stages and also estimate the disease infection stage.

Efficient sign language recognition system and dataset creation method based on deep learning and image processing

New deep-learning architectures are created every year, achieving state-of-the-art results in image recognition and leading to the belief that, in a few years, complex tasks such as sign language translation will be considerably easier, serving as a communication tool for the hearing-impaired community. On the other hand, these algorithms still need a lot of data to be trained and the dataset creation process is expensive, time-consuming, and slow. Thereby, this work aims to investigate techniques of digital image processing and machine learning that can be used to create a sign language dataset effectively. We argue about data acquisition, such as the frames per second rate to capture or subsample the videos, the background type, preprocessing, and data augmentation, using convolutional neural networks and object detection to create an image classifier and comparing the results based on statistical tests. Different datasets were created to test the hypotheses, containing 14 words used daily and recorded by different smartphones in the RGB color system. We achieved an accuracy of 96.38% on the test set and 81.36% on the validation set containing more challenging conditions, showing that 30 FPS is the best frame rate subsample to train the classifier, geometric transformations work better than intensity transformations, and artificial background creation is not effective to model generalization. These trade-offs should be considered in future work as a cost-benefit guideline between computational cost and accuracy gain when creating a dataset and training a sign recognition model.