Face Mask Detection Using Image Processing and Convolutional Neural Networks

Yedukondalu, Jammisetty; Singh, T Yogesh; Sharma, Diksha; Singh, Ram Sewak; Sharma, Lakhan Dev

doi:10.1109/cict56698.2022.9997821

Cited by 4 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a challenging task in computer vision, as small objects can be difficult to detect due to their size and the presence of other objects or clutter in the image. Small-scale object detection [1] is important for various applications, such as surveillance, self-driving cars, and image recognition. In surveillance, small objects such as weapons or suspicious packages can be difficult to spot, making small-scale object detection crucial for security purposes.…”

Section: Introductionmentioning

confidence: 99%

Extreme small-scale prediction head-based efficient YOLOV5 for small-scale object detection

Gowthami,

Blessy

2024

Eng. Res. Express

View full text Add to dashboard Cite

Recognition models achieved exceptional performance in object detection during the computer vision era, but they still rely on Small-Scale Object Detection (SSOD). SSOD remains difficult due to the variety of shapes and orientations of the object, as well as fixed prediction heads at the detection stage. To overcome these challenges, our paper proposed an auto anchor module, Spatial Pyramid Pooling Faster layer (SPPF) in the feature refinement network, and an extremely small-scale prediction head at the detection stage of the model. With the auto anchor feature, the model is able to adjust the anchor boxes dynamically during training, which can improve its overall performance. The SPPF layer divides the feature map into five levels of pyramids, where each level corresponds to a different scale. This enables the model to detect objects of different sizes by pooling features from all the levels of the pyramid. Extreme Small Scale prediction head is specifically designed for small object detection and uses anchor boxes with small sizes and feature re-sampling techniques to improve the accuracy of SSOD. We perform quantitative evaluations on the VOC and achieved 85.4 mAP, 79.8% precision, 80.3% recall, and 80% F1-score. These reliable results prove that the suggested model has greater performance compared with existing models in detecting small-scale objects.

show abstract

Section: Introductionmentioning

confidence: 99%