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 × 812 × 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.

计算机视觉是对象检测的一个跨学科领域。物体检测继电器是协助监视，车辆检测和姿态估计的重要组成部分。在这项工作中，我们提出了一种新颖的只看一次的深层（深层YOLO V3）方法来检测多对象。这种方法在培训和测试阶段着眼于整个框架。它遵循了基于回归的技术，该技术使用概率模型来定位对象。在这种情况下，我们构造106个卷积层，然后构造2个完全连接的层，并输入812×812×3的输入大小，以检测小尺寸的无人机。我们以一半的分辨率预训练卷积层以进行分类，然后将分辨率加倍以进行检测。每层的过滤器数量将设置为16。最后一个缩放层的过滤器数量大于16，以改善小物体检测的能力。这种构造使用上采样技术将不需要的光谱图像改善为现有信号，并在特定位置重新缩放特征。它清楚地表明，向上采样可以检测到小物体。它实际上提高了采样率。首选YOLO架构是因为它考虑的是更少的内存资源和计算成本，而不是更多的过滤器数量。所提出的系统经过设计和培训，可以执行一种称为无人机的单一类型的类，并且可以使用基于嵌入式系统的深层YOLO进行对象检测和跟踪。提出的YOLO方法可以更好地预测每个网格单元的多个边界框。