Apple detection during different growth stages in orchards using the improved YOLO-V3 model

TL;DR: This study can provide technical reference for the detection of the ear loss rate of intelligent maize harvester ears in the field and improve the accuracy of the YOLO-V4 model.

Abstract: Accurate detection of pomegranate maturity plays a crucial role in optimizing harvesting decisions and enhancing economic benefits. Conventional approaches encounter significant challenges in complex agricultural scenarios, including limited feature representation capabilities, singular attention mechanisms, and insufficient multi-scale information fusion. This study presents the DFMA-DETR algorithm, which establishes an end-to-end detection framework through dual-domain feature modulation and enhanced attention mechanisms. The core contributions include: (1) Development of the DFMB-Net backbone network that employs spatial-frequency collaborative processing to model pomegranate surface textures, color variations, and morphological characteristics. (2) Construction of the EAFF enhanced attention feature fusion module that integrates adaptive sparse attention mechanisms with multi-scale feature adapters, effectively addressing feature representation challenges under complex background interference; (3) Introduction of the AIUP adaptive interpolation upsampling processor and MFCM multi-branch feature convolution module, substantially improving feature alignment accuracy and multi-scale representation performance. Experimental validation on the constructed PGSD-5K dataset demonstrates that DFMA-DETR achieves detection accuracies of 90.23% mAP@50 and 76.40% mAP@50-95, representing improvements of 3.13% and 3.06% respectively over the baseline RT-DETR model, while maintaining relatively low model complexity. Cross-dataset validation further confirms the superior generalization performance of the proposed approach. This research provides an effective solution for advancing intelligent detection technologies in precision agriculture.

TL;DR: The research proposes a system for crop-weed detection, depth estimation and disease diagnosis using YOLO and Darknet for Agribot, a precision farming robot. It employs deep learning techniques for object detection, distance estimation and disease diagnosis.

TL;DR: This paper proposes LiSTM, a novel LiDAR 3D object detection framework that incorporates temporal motion estimation to enhance spatial-temporal feature learning, achieving superior detection performance on Waymo and nuScenes datasets through motion-guided feature aggregation and dual correlation weighting.

TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.

TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.

TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.

TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.