# DP-YOLO: Effective Improvement Based on YOLO Detector
## 1 Introduction  
Computer vision uses computer technology to process and analyze digital images and videos. Deep learning has significantly advanced object detection, a fundamental task in this field. Its widespread use spans various industries, including autonomous driving, security monitoring, industrial manufacturing, medical diagnosis, retail, and agriculture, due to improved accuracy and efficiency.  
Object detection algorithms can be broadly categorized into two types: Two-stage algorithms [1; 2; 3] that involve candidate region extraction and classifier prediction, and one-stage algorithms [4; 5; 6] that combine feature extraction and target classifier prediction directly. Among single-stage algorithms, the YOLO family, particularly YOLOv5, balanced accuracy, speed, ease of training, and deployment. In addition to its excellent scalability resulting from the integration of numerous tricks and modularized code, YOLOv5 also provides lighter versions. But we are still in the predicament that although the detection ability of large CNN [7] or transformer [8; 9]-based detection algorithms is very close to human or even beyond, their huge computational cost makes it difficult to be widely used in production and life. The vision tasks will be oriented to both the colorful nature and also be applied to some special situations in specific domains, which puts a higher demand on detectors with less computational resource consumption. When we apply YOLOv5 and other algorithms trained on generalized datasets to domain-specific datasets,some limitations arise, including poor detection of small targets, insufficient adaptation to complex backgrounds, and limited ability to deal with target deformation and attitude changes. Addressing these issues while maintaining the algorithm's efficiency has emerged as a critical focus in object detection.  
While domain-specific dataset detection targets are not used for a myriad of possibilities as are silhouettes of human activity and nature, most of them have unique image characteristics in addition to often being characterized by limited data and category imbalance. For instance, microscopic images of urine sediment lack prominent features visible to the human eye. Additionally, such datasets exhibit an increased number of small targets, category confusion, and significant morphological and gestural differences within similar categories. To enhance detection accuracy in these complex and specific scenarios, this paper builds upon the YOLOv5 detection framework by incorporating deformable convolutions into the backbone network more efficiently. Furthermore, improvements are made in the label assignment strategy, and the proposed method's effectiveness is validated in detecting these distinct images.  
The contributions of our work can be listed as follows:  
1. Modifying the expansion policy of positive samples in label assignment. Combined with the previous work, the quality of positive samples can be improved by considering the effective receptive field characteristics, which can be used to update the parameters in the deep network to obtain a better detector.
2. Introducing deformable convolution into the backbone network of YOLOv5s in a more cost-effective way. By leveraging the distinct properties of various deformable convolution operators to design and balance them, a substantial enhancement in detection accuracy is attained on the generalized dataset. Remarkably, this improvement is achieved with only a minor increase in the number of parameters and computational effort required.
3. The improved detector DP-YOLO under the action of both methods was validated and analyzed on a urine sediment dataset for a specific assay domain, in addition to being validated on the generic dataset COCO2017.