Assessing the Capability of YOLO- and Transformer-based Object Detectors for Real-time Weed Detection

• URL: http://arxiv.org/abs/2501.17387v2
• Date: Thu, 30 Jan 2025 09:56:55 GMT
• Title: Assessing the Capability of YOLO- and Transformer-based Object Detectors for Real-time Weed Detection
• Authors: Alicia Allmendinger, Ahmet Oğuz Saltık, Gerassimos G. Peteinatos, Anthony Stein, Roland Gerhards,
• Abstract summary: All available models of YOLOv8, YOLOv9, YOLOv10, and RT-DETR are trained and evaluated with images from a real field situation. The results demonstrate that while all models perform equally well in the metrics evaluated, the YOLOv9 models stand out in terms of their strong recall scores. RT-DETR models, especially RT-DETR-l, excel in precision with reaching 82.44 % on dataset 1 and 81.46 % in dataset 2.
• Score: 0.0
• License:
• Abstract: Spot spraying represents an efficient and sustainable method for reducing the amount of pesticides, particularly herbicides, used in agricultural fields. To achieve this, it is of utmost importance to reliably differentiate between crops and weeds, and even between individual weed species in situ and under real-time conditions. To assess suitability for real-time application, different object detection models that are currently state-of-the-art are compared. All available models of YOLOv8, YOLOv9, YOLOv10, and RT-DETR are trained and evaluated with images from a real field situation. The images are separated into two distinct datasets: In the initial data set, each species of plants is trained individually; in the subsequent dataset, a distinction is made between monocotyledonous weeds, dicotyledonous weeds, and three chosen crops. The results demonstrate that while all models perform equally well in the metrics evaluated, the YOLOv9 models, particularly the YOLOv9s and YOLOv9e, stand out in terms of their strong recall scores (66.58 % and 72.36 %), as well as mAP50 (73.52 % and 79.86 %), and mAP50-95 (43.82 % and 47.00 %) in dataset 2. However, the RT-DETR models, especially RT-DETR-l, excel in precision with reaching 82.44 \% on dataset 1 and 81.46 % in dataset 2, making them particularly suitable for scenarios where minimizing false positives is critical. In particular, the smallest variants of the YOLO models (YOLOv8n, YOLOv9t, and YOLOv10n) achieve substantially faster inference times down to 7.58 ms for dataset 2 on the NVIDIA GeForce RTX 4090 GPU for analyzing one frame, while maintaining competitive accuracy, highlighting their potential for deployment in resource-constrained embedded computing devices as typically used in productive setups.

Related papers

• Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection [0.0]
  This paper presents a comprehensive evaluation of object detection models, including YOLOv9, YOLOv10, and RT-DETR, for weed detection in smart-spraying applications. The performance of these models is compared based on mean Average Precision (mAP) scores and inference times on different GPU and CPU devices.
  arXiv  Detail & Related papers  (2024-12-18T04:15:32Z)
• Evaluating the Evolution of YOLO (You Only Look Once) Models: A Comprehensive Benchmark Study of YOLO11 and Its Predecessors [0.0]
  This study presents a benchmark analysis of various YOLO (You Only Look Once) algorithms, from YOLOv3 to the newest addition, YOLO11. It evaluates their performance on three diverse datasets: Traffic Signs (with varying object sizes), African Wildlife (with diverse aspect ratios and at least one instance of the object per image), and Ships and Vessels (with small-sized objects of a single class)
  arXiv  Detail & Related papers  (2024-10-31T20:45:00Z)
• Performance Evaluation of YOLOv8 Model Configurations, for Instance Segmentation of Strawberry Fruit Development Stages in an Open Field Environment [0.0]
  This study evaluates the performance of YOLOv8 model configurations for instance segmentation of strawberries into ripe and unripe stages in an open field environment. The YOLOv8n model demonstrated superior segmentation accuracy with a mean Average Precision (mAP) of 80.9%, outperforming other YOLOv8 configurations.
  arXiv  Detail & Related papers  (2024-08-11T00:33:45Z)
• Quantizing YOLOv7: A Comprehensive Study [0.0]
  This paper studies the effectiveness of a variety of quantization schemes on the pre-trained weights of the state-of-the-art YOLOv7 model. Results show that using 4-bit quantization coupled with the combination of different granularities results in 3.92x and 3.86x memory-saving for uniform and non-uniform quantization.
  arXiv  Detail & Related papers  (2024-07-06T03:23:04Z)
• YOLOv10: Real-Time End-to-End Object Detection [68.28699631793967]
  YOLOs have emerged as the predominant paradigm in the field of real-time object detection. The reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs. We introduce the holistic efficiency-accuracy driven model design strategy for YOLOs.
  arXiv  Detail & Related papers  (2024-05-23T11:44:29Z)
• YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection [63.36722419180875]
  We provide an efficient and performant object detector, termed YOLO-MS. We train our YOLO-MS on the MS COCO dataset from scratch without relying on any other large-scale datasets. Our work can also serve as a plug-and-play module for other YOLO models.
  arXiv  Detail & Related papers  (2023-08-10T10:12:27Z)
• Exploring the Effectiveness of Dataset Synthesis: An application of Apple Detection in Orchards [68.95806641664713]
  We explore the usability of Stable Diffusion 2.1-base for generating synthetic datasets of apple trees for object detection. We train a YOLOv5m object detection model to predict apples in a real-world apple detection dataset. Results demonstrate that the model trained on generated data is slightly underperforming compared to a baseline model trained on real-world images.
  arXiv  Detail & Related papers  (2023-06-20T09:46:01Z)
• DeepSeaNet: Improving Underwater Object Detection using EfficientDet [0.0]
  This project involves implementing and evaluating various object detection models on an annotated underwater dataset. The dataset comprises annotated image sequences of fish, crabs, starfish, and other aquatic animals captured in Limfjorden water with limited visibility. I compare the results of YOLOv3 (31.10% mean Average Precision (mAP)), YOLOv4 (83.72% mAP), YOLOv5 (97.6%), YOLOv8 (98.20%), EfficientDet (98.56% mAP) and Detectron2 (95.20% mAP) on the same dataset.
  arXiv  Detail & Related papers  (2023-05-26T13:41:35Z)
• End-to-end deep learning for directly estimating grape yield from ground-based imagery [53.086864957064876]
  This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards. Three model architectures were tested: object detection, CNN regression, and transformer models. The study showed the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale.
  arXiv  Detail & Related papers  (2022-08-04T01:34:46Z)
• A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery [94.78943497436492]
  We present YOLO-S, a simple, fast and efficient network for small target detection. YOLO-S exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation. YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
  arXiv  Detail & Related papers  (2022-04-05T16:29:49Z)
• A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery [56.10033255997329]
  We propose a novel deep learning method based on a Convolutional Neural Network (CNN) It simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops.
  arXiv  Detail & Related papers  (2020-12-31T18:51:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.