DC-YOLOv8: Small Size Object Detection Algorithm Based on Camera Sensor

Abstract

Traditional camera sensors rely on human eyes for observation. However, the human eye 1
 is prone to fatigue when observing targets of different sizes for a long time in complex scenes, and 2
 human cognition is limited, which often leads to judgment errors and greatly reduces the efficiency. 3
 Target recognition technology is an important technology to judge the target category in camera 4
 sensor. In order to solve this problem, a small size target detection algorithm for special scenarios was 5
 proposed by this paper. Its advantage is that this algorithm not only has higher precision for small 6
 size target detection, but also can ensure that the detection accuracy of each size is not lower than the 7
 existing algorithm. In this paper, a new down-sampling method was proposed, which could better 8
 preserve the context feature information. The feature fusion network was improved to effectively 9
 combine shallow information and deep information. A new network structure was proposed to 10
 effectively improve the detection accuracy of the model. In terms of accuracy, it is better than: YOLOX, 11
 YOLOXR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny and YOLOv8.Three authoritative public data sets 12
 were used in this experiment: a) On Visdron data sets (small size targets), DC-YOLOv8 is 2.5% more 13
 accurate than YOLOv8. b) On Tinyperson data sets (minimal size targets), DC-YOLOv8 is 1% more 14
 accurate than YOLOv8. c) On PASCAL VOC2007 data sets (Normal size target), DC-YOLOv8 is 0.5% 15
 more accurate than YOLOv8.

Real-Time Vehicle License Plate Recognition (VLPR) Using Deep CNN

“…Furthermore, YOLOv8's transition to an anchor-free detection approach, utilizing a task-aligned assigner, enables more dynamic and precise object detection, adapting more accurately to the physical dimensions of detected objects. These innovations not only enhance the model's accuracy but also contribute to its robustness, making it highly effective across diverse and challenging environments [15]. The implementation of the YOLOv8 model for license plate (LP) detection commenced with meticulous configuration of the network architecture, specifically tailored to LP identification needs.…”