MiniYOLO: A lightweight object detection algorithm that realizes the trade‐off between model size and detection accuracy

Abstract

The object detection task is to locate and classify objects in an image. The current state‐of‐the‐art high‐accuracy object detection algorithms rely on complex networks and high computational cost. These algorithms have high requirements on the memory resource and computing capability of the deployed device, and are difficult to apply to mobile and embedded devices. Through the depthwise separable convolution and multiple efficient network structures, this paper designs a lightweight backbone network and two different multiscale feature fusion structures, and proposes a lightweight one‐stage object detection algorithm—MiniYOLO. With the model size of only 4.2 MB, MiniYOLO still maintains a high detection accuracy, realizing the trade‐off between the model size and detection accuracy. Experimental results on MS COCO 2017 data set show that compared to the state‐of‐the‐art PP‐YOLO‐tiny, MiniYOLO achieves higher mAP with the same model size. Compared with other lightweight object detection algorithms, MiniYOLO has certain advantages in detection accuracy or model size. The code associated with this paper can be downloaded from https://github.com/CaedmonLY/MiniYOLO.

Lightweight small target detection based on aerial remote sensing images

“…Against the backdrop of the rapid upgrading of computer vision technology, the application of automated scanning of lightweight targets in rescuing people in distress and predicting natural disasters has been developed [1,2]. However, most of these types of targets are lightweight, and target capture algorithms are prone to generating duplicate pixels during operation, reducing the algorithm's speed by generating redundant information.…”