YOLOv5 with ConvMixer Prediction Heads for Precise Object Detection in Drone Imagery

Baidya, Ranjai; Jeong, Heon

doi:10.3390/s22218424

Cited by 27 publications

(14 citation statements)

References 51 publications

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“…The network structure of YOLOv5 is made up of Inputs, Backbone, Neck, and Prediction [20]. The input stage adopts techniques such as Mosaic data augmentation [21], adaptive anchor box calculation, and adaptive image scaling.…”

Section: The Network Architecture Of Yolov5mentioning

confidence: 99%

A novel small object detection algorithm for UAVs based on YOLOv5

Li,

Zhang,

Liu

et al. 2024

Phys. Scr.

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Due to the advances in deep learning, artificial intelligence is widely utilized. Technologies frontier, including computer vision, represented by object detection, have endowed unmanned aerial vehicles (UAVs) with autonomous perception, analysis, and decision-making capabilities. UAVs extensively utilized in numerous fields including photography, industry and agriculture, surveillance, disaster relief, and play an important role in real life. However, current object detection algorithms encounter challenges when it comes to detecting small objects in images captured by UAVs. The small size of the objects, high density, low resolution, and few features make it difficult for the algorithms to achieve high detection accuracy and are prone to miss and false detections especially when detecting small objects. For the case of enhancing the performance of UAV detection on small objects, a novel small object detection algorithm for UAVs adaptation based on YOLOv5s (UA-YOLOv5s) was proposed. 1) To achieve effective small-sized objects detection and better detection performance, a more accurate small object detection (MASOD) structure was adopted. 2) To boost the detection accuracy and generalization ability of the model, a multi-scale feature fusion (MSF) approach was introduced, which fused the feature information of the shallow layers of the backbone and the neck. 3) Towards enhancing the model stability properties and feature extraction capability, a more efficient and stable convolution residual Squeeze-and-Excitation (CRS)module was introduced. Compared with the YOLOv5s, mAP@0.5 was achieved an impressive improvement of 7.2%. Compared with the YOLOv5l, mAP@0.5 increased by 1.0%, and GFLOPs decreased by 69.1%. Compared to the YOLOv3, mAP@0.5 decreased by 0.2% and GFLOPs by 78.5%. The study's findings demonstrate that the proposed UA-YOLOv5s significantly enhance the object detection performance of UAVs campared to YOLOv5s.

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Section: The Network Architecture Of Yolov5mentioning

confidence: 99%

A novel small object detection algorithm for UAVs based on YOLOv5

Li,

Zhang,

Liu

et al. 2024

Phys. Scr.

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“…Over the past decade, artificial intelligence has led to significant progress in the domain of computer vision, automating image and video analysis tasks. Among computer vision methods, Convolutional Neural Networks (CNNs) are particularly promising for future advances in automating wildlife monitoring [6,[12][13][14][15][16][17][18]. Corcoran et al [3] concluded that when implementing automatic detection, fixed-winged drones with RGB sensors were ideal for detecting larger animals in open terrain, whereas, for small, elusive animals in more complex habitats, multi-rotor systems with infrared (IR) or thermal infrared sensors are the better choice, especially when monitoring cryptic and nocturnal animals.…”

Section: Automatic Detection and Computer Visionmentioning

confidence: 99%

Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring

Povlsen,

Bruhn,

Durdevic

et al. 2023

Drones

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Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP) compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models’ precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.

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“…AP is the area under the precision-recall curve and provides a measure of the quality of the model’s predictions at different recall levels 38 …”

Section: Performance Metricsmentioning

confidence: 99%

Refined single-stage object detection deep-learning technique for chilli leaf disease detection

2023

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YOLOv5 with ConvMixer Prediction Heads for Precise Object Detection in Drone Imagery

Cited by 27 publications

References 51 publications

A novel small object detection algorithm for UAVs based on YOLOv5

A novel small object detection algorithm for UAVs based on YOLOv5

Using YOLO Object Detection to Identify Hare and Roe Deer in Thermal Aerial Video Footage—Possible Future Applications in Real-Time Automatic Drone Surveillance and Wildlife Monitoring

Refined single-stage object detection deep-learning technique for chilli leaf disease detection

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