The new loss function is introduced in the YOLO v5 which improves the performance of object detection and the CIoU-YOLO v5 method attained the Average Precision (AP) of 44.7% on MS-COCO 2017 dataset and 53.7% AP on KITTI dataset which is superior than Dual-path Lightweight Module (DLM).
The main aim of object detection is to extract the feature for different sizes trough hierarchically stacking the multiple scale feature maps. The 2 Dimension (2D) object detection is the difficult in facilitates the perceptron system to knowing the environment. Though, that is not easy for deciding the transmit semantic data to low-level layers when minimizing loss of semantic data of high-level features. In this research, the Center Intersection of Union loss with You Only Look Once (CIoU-YOLO) method for detecting the objects. The datasets used for detecting the objects are MS-COCO 2017 and KITTI and the images in the dataset are pre-processed by using the point clouds. Then, the detection is performed by using the CIoU – YOLO v5 method. The new loss function is introduced in the YOLO v5 which improves the performance of object detection. The CIoU-YOLO v5 method attained the Average Precision (AP) of 44.7% on MS-COCO 2017 dataset and 53.7% AP on KITTI dataset which is superior than Dual-path Lightweight Module (DLM).