Cotton Stubble Detection Based on Improved YOLOv3

Abstract

The stubble after cotton harvesting was used as the detection object to achieve the visual navigation operation for residual film recovery after autumn. An improved (You Only Look Once v3) YOLOv3-based target detection algorithm was proposed to detect cotton stubble. First, field images of residual film recycling were collected. Considering the inconsistency between stubble size and shape, a segmented labeling data set of stubble is proposed. Secondly, the Darknet-53 backbone of the original YOLOv3 network is improved to accommodate tiny targets. Next, the prediction anchor box of the improved detection backbone is clustered using K-means++, and the size of the prediction anchor box suitable for improved YOLOv3 is determined. Finally, for the false detection points after detection, a mean value denoising method is used to remove the false detection points. Feature points are extracted from the denoised stubble, and the candidate points are fitted by the least square method to obtain the navigation line. The optimal model with a mean average precision (mAP) of 0.925 is selected for testing at the test stage. The test results show that the algorithm in this article can detect the stubble of residual film recovery images at different locations, different time periods, and different camera depression angles without misdetection. The detection speed of a single image is 98.6 ms. Based on an improvement over YOLOv3, the improved model has a significantly higher detection rate in different scenarios than YOLOv3. This can provide practical technical support for the visual navigation of residual film recovery.

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

“…Furthermore, Liu et al [23] attained 96.4% mAP for tomato positioning by using YOLOv3 to replace circular boundary boxes with traditional rectangular boundary boxes. Yang et al [24] used K-means++ and the mean denoising method to identify cotton residue after harvesting, improving the precision of YOLOv3. Gai et al [25] enhanced the mAP of detecting cherries by 15% by modifying the labeled boxes with DenseNet in YOLOv4.…”

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

“…Another category is single-stage algorithms, for example, You Only Look Once (YOLO) series algorithms [10,11], single shot multibox detector (SSD) algorithms [12] etc. The representative models are: Tu Renwei et al [13] improved the YOLO v3 algorithm by pruning the original three feature heads to two and using the K-means++ clustering method to calculate the anchor point values of the dataset to solve the slow detection speed, low efficiency, and inability to adapt to the low light environment of the original algorithm.…”

SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation

“…As a research hotspot in the field of deep learning, convolutional neural networks have achieved excellent research results in agriculture in recent years, and they are widely used in various agricultural vision applications [30][31][32][33][34] and have demonstrated higher accuracy and wider applicability than other normal algorithms [35]. Deep learning-based crop row segmentation methods have also achieved excellent results: Silva [36] et al and Cao [37] et al used the Unet [38] model, and the improved Enet [39] model implemented segmentation of crop rows from an open dataset containing images of sugar beet rows in various complex environments.…”