OSDDY: embedded system-based object surveillance detection system with small drone using deep YOLO

Computer vision is an interdisciplinary domain for object detection. Object detection relay is a vital part in assisting surveillance, vehicle detection and pose estimation. In this work, we proposed a novel deep you only look once (deep YOLO V3) approach to detect the multi-object. This approach looks at the entire frame during the training and test phase. It followed a regression-based technique that used a probabilistic model to locate objects. In this, we construct 106 convolution layers followed by 2 fully connected layers and 812 x 812 x 3 input size to detect the drones with small size. We pre-train the convolution layers for classification at half the resolution and then double the resolution for detection. The number of filters of each layer will be set to 16. The number of filters of the last scale layer is more than 16 to improve the small object detection. This construction uses up-sampling techniques to improve undesired spectral images into the existing signal and rescaling the features in specific locations. It clearly reveals that the up-sampling detects small objects. It actually improves the sampling rate. This YOLO architecture is preferred because it considers less memory resource and computation cost rather than more number of filters. The proposed system is designed and trained to perform a single type of class called drone and the object detection and tracking is performed with the embedded system-based deep YOLO. The proposed YOLO approach predicts the multiple bounding boxes per grid cell with better accuracy. The proposed model has been trained with a large number of small drones with different conditions like open field, and marine environment with complex background.

Automated summary

Computer vision is a vital interdisciplinary domain for object detection, where the deep learning approach deep YOLO V3 is effective for multi-object detection, especially for small drones. The system improves accuracy in small object detection using convolution layers and up-sampling techniques.