An improved algorithm for small object detection based on YOLO v4 and multi-scale contextual information

In real life, object detection is widely applied and plays a significant part in the field of computer vision. However, when detecting small objects, the advanced You Only Look Once v4 (YOLO v4) model often misses or incorrectly detects them due to the limited resolution and unclear features of the small objects, which reduces the detection accuracy. A small object detection algorithm based on YOLO v4 and Multi-scale Contextual information and Soft-CIOU loss function, called MCS-YOLO v4, is proposed in this paper. MCS-YOLO v4 adds a scale detection to the existing three scales to obtain rich location information. To enhance the ability of network to locate and classify the object, MCS-YOLO v4 introduces an expanded field-of-perception block. This block obtains the object contextual features and integrates them with the convolutional features to obtain more robust and discriminative features. To reduce the influence of insignificant information in images on small object, the attention module is introduced in the neck part of YOLO v4. To further improve the detection accuracy of small objects, the Soft-CIOU loss function is proposed. The aspect ratio weight factor is introduced into the weight function of the CIOU (Complete-IOU) loss function, while the Euclidean distance is subjected to the open-root operation, which improves the contribution of small objects to the loss function and enhances the learning ability of the network for small objects. The experimental results on the publicly available small object datasets verify that the proposed model has better detection effect than other detection models in detecting small objects.