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

Abstract

Traditional camera sensors rely on human eyes for observation. However, human eyes are prone to fatigue when observing objects of different sizes for a long time in complex scenes, and human cognition is limited, which often leads to judgment errors and greatly reduces efficiency. Object recognition technology is an important technology used to judge the object’s category on a camera sensor. In order to solve this problem, a small-size object detection algorithm for special scenarios was proposed in this paper. The advantage of this algorithm is that it not only has higher precision for small-size object detection but also can ensure that the detection accuracy for each size is not lower than that of the existing algorithm. There are three main innovations in this paper, as follows: (1) A new downsampling method which could better preserve the context feature information is proposed. (2) The feature fusion network is improved to effectively combine shallow information and deep information. (3) A new network structure is proposed to effectively improve the detection accuracy of the model. From the point of view of detection accuracy, it is better than YOLOX, YOLOR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny, and YOLOv8. Three authoritative public datasets are used in these experiments: (a) In the Visdron dataset (small-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 2.5%, 1.9%, and 2.1% higher than those of YOLOv8s, respectively. (b) On the Tinyperson dataset (minimal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 1%, 0.2%, and 1.2% higher than those of YOLOv8s, respectively. (c) On the PASCAL VOC2007 dataset (normal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 0.5%, 0.3%, and 0.4% higher than those of YOLOv8s, respectively.

Wavelet scattering‐ and object detection‐based computer vision for identifying dengue from peripheral blood microscopy

“…The C2f module integrates high‐level features with contextual information, enhancing detection accuracy. The SPPF, the last layer of the backbone, processes features at various scales (along with subsequent convolution layers), which increases model inference speed 59,60 . In the neck, upper layers acquire more information due to additional network layers, whereas lower layers preserve location information due to fewer convolution layers.…”

“…The SPPF, the last layer of the backbone, processes features at various scales (along with subsequent convolution layers), which increases model inference speed. 59,60 In the neck, upper layers acquire more information due to additional network layers, whereas lower layers preserve location information due to fewer convolution layers. In YOLOv8, the traditional YOLO neck architecture is replaced with a novel C2f module that incorporates feature pyramid network (FPN) and path aggregation network (PAN) architectures.…”

BD-YOLO: detection algorithm for high-resolution remote sensing images

“…This paper presents an enhanced algorithm for the one-stage algorithm YOLOv8 [22], which was newly released in 2023. It offers real-time performance while maintaining high accuracy, surpassing two-stage algorithms in speed and outperforming other one-stage algorithms in accuracy.…”

STAE‐YOLO: Intelligent detection algorithm for risk management of construction machinery intrusion on transmission lines based on visual perception

“…To comprehensively assess the performance of the models, we selected several state-of-the-art (SOTA) object detection networks for further comparison, each representing the most advanced algorithms in the current field. These include the twostage detection network Faster R-CNN [39], the single-stage detection network RetinaNet [45], SSD [42], YOLOv5 [40], YOLOv8 [41], the single-stage anchor-free network CenterNet [44], the end-to-end detection network DETR [43], and the single-stage anchor-free network FCOS [46]. Additionally, we replaced our improvements on the commonly used YOLOv5-S model, incorporating the YOLOv5s-Swin Transformer and YOLOv5s-SA as two comparative enhanced models, and validated all obtained results.…”