YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection

Kong, Lingren; Wang, Jianzhong; Zhao, Peng

doi:10.1109/access.2022.3177628

Cited by 50 publications

(16 citation statements)

References 42 publications

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“…YOLOv5x-ALL- GHOST added GhostNet in both head and backbone and got 0.6% less map@0.5 than GAANet [26]. GhostNet feature extraction networking was embedded in YOLOv3 [27] that achieved 8.3% less map@0.5 than GAANet.…”

Section: Comparison With State-of-the Artmentioning

confidence: 99%

TransLearn-YOLOx: Improved-YOLO with Transfer Learning for Fast and Accurate Multiclass UAV Detection

Khan¹,

Dil²,

Misbah³

et al. 2023

Preprint

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The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multi-class UAV's classification performance using single-shot object detection algorithms YOLOv5 and YOLOv7. The transfer learning is employed for performance improvement and rapid training with improved results. We customized a multi-class dataset containing multi-rotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the integration of transfer learning has achieved good results, with an overall best average-classification precision of 94\%, an average recall of 93.1\%, a mAP$@$0.5 average of 95.3\%, and an average F1 score of 92.33\%. The dataset and code are available as an open source: https://github.com/ZeeshanKaleem/YOLOV5-Large-vs-YOLOV7.git

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Section: Comparison With State-of-the Artmentioning

confidence: 99%

TransLearn-YOLOx: Improved-YOLO with Transfer Learning for Fast and Accurate Multiclass UAV Detection

Khan¹,

Dil²,

Misbah³

et al. 2023

Preprint

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“…For example Kong et al, proposed YOLO-G, which builds upon the YOLO-v3 work and was applied to a data set for detection of soldiers in various scenes. 11 Yang et al investigated transfer learning for military object recognition when limited training data is available. 35 Du et al, presented a lightweight target detection method to address the limited hardware resources available at a weapon equipment platform.…”

Section: Related Workmentioning

confidence: 99%

“…YOLO, 4 R-CNN, 5 and SSD, 6 with promising results for autonomous driving, 7 robotic perception, 8 search and rescue, 9 surveillance, 10 and military object detection. 11 However, detection of small object remains an open challenge. 12 With small objects we refer to the small number of pixels that make up the objects in an image, which can either be objects far away from the imaging plane (e.g.…”

Section: Introductionmentioning

confidence: 99%

Leveraging temporal context in deep learning methodology for small object detection

Heslinga,

Ruis,

Ballan

et al. 2023

Artificial Intelligence for Security and Defence Applications

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Automated object detection is becoming more relevant in a wide variety of applications in the military domain. This includes the detection of drones, ships, and vehicles in video and IR video. In recent years, deep learningbased object detection methods, such as YOLO, have shown to be promising in many applications for object detection. However, current methods have limited success when objects of interest are small in number of pixels, e.g. objects far away or small objects closer by. This is important, since accurate small object detection translates to early detection and the earlier an object is detected the more time is available for action. In this study, we investigate novel image analysis techniques that are designed to address some of the challenges of (very) small object detection by taking into account temporal information. We implement six methods, of which three are based on deep learning and use the temporal context of a set of frames within a video. The methods consider neighboring frames when detecting objects, either by stacking them as additional channels or by considering difference maps. We compare these spatio-temporal deep learning methods with YOLO-v8 that only considers single frames and two traditional moving object detection methods. Evaluation is done on a set of videos that encompasses a wide variety of challenges, including various objects, scenes, and acquisition conditions to show real-world performance.

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“…Lightweight technology in the field to reduce the field equipment computing resources requirements at the same time, can achieve fast recognition, in the face of largescale planting scale recognition task can have a higher operational efficiency, and for the moving target of its fast recognition ability to effectively prevent the loss of the target [17]. However, lightweight to a certain extent reduces the accuracy of the target detection model, which will inevitably lead to an increase in the demand and workload of the subsequent processing, so it is also necessary to carry out the improvement of accuracy [18].…”

Section: Introductionmentioning

confidence: 99%

Accurate recognition of jujube tree trunks based on CLAHE image enhancement and improved YOLOv8

Ling,

Wang,

et al. 2023

Preprint

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Background Agricultural image acquisition and target detection are the key links of agricultural precision and intelligence. Facing the practical problems of complex orchard environment and large workload, the existing target detection models have problems such as large number of parameters, slow detection speed, low detection accuracy and poor generalization. Methods In this paper, an improved YOLOv8 target detection model facing the complex environment of orchards is proposed. Firstly, the dataset is efficiently extracted using the key frame extraction algorithm, and the dataset is enhanced using the CLAHE image enhancement method; secondly, the backbone network of the YOLOv8 is replaced with the GhostNetv2 architecture, the GhostConv and GhostBottleneck modules are utilized for the lightweight modification. In addition, the CA_H attention mechanism is improved and added to the Neck part of YOLOv8 network architecture to form YOLOv8s-GhostNetv2-CA_H target detection model. Finally, the effectiveness of the improved model is verified using enhanced dataset comparison tests, ablation tests, and comparison tests with classical methods such as Faster R-CNN and YOLOv5s. Results The average precision of the enhanced dataset over the original dataset rises from 81.2–90.1%; the YOLOv8s-GhostNetv2-CA_H model proposed in this paper reduces the model size by 19.5% compared to the YOLOv8s base model, precision increased by 2.4–92.3%, recall increased by 1.4%, "mAP@0.5" increased by 1.8%, and FPS is 17.1% faster. Conclusions In the complex environment of jujube garden image enhancement has a greater impact on target detection accuracy enhancement, and, the performance of the method proposed in this paper is better than other related algorithms in practical applications.

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YOLO-G: A Lightweight Network Model for Improving the Performance of Military Targets Detection

Cited by 50 publications

References 42 publications

TransLearn-YOLOx: Improved-YOLO with Transfer Learning for Fast and Accurate Multiclass UAV Detection

TransLearn-YOLOx: Improved-YOLO with Transfer Learning for Fast and Accurate Multiclass UAV Detection

Leveraging temporal context in deep learning methodology for small object detection

Accurate recognition of jujube tree trunks based on CLAHE image enhancement and improved YOLOv8

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