Research on Object Detection Method Based on FF-YOLO for Complex Scenes

Baoyuan, Chen; Liu, Yitong; Sun, Kun

doi:10.1109/access.2021.3108398

Cited by 13 publications

(6 citation statements)

References 25 publications

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“…In order to verify the effectiveness of deconvolution cascade model (hereinafter referred to as DC module) and GA-RPN in UAV aerial target detection algorithms in this algorithm, a multi-scale comparison scheme was designed based on VisDrone target detection data, as shown in Table 3.2 and Figure 3.1. The test experiment was conducted in the VisDrone test dev dataset (1610 UAV aerial images, including various situations in the VisDrone dataset), set Faster Rcnn (Resnet50+RPN) as the baseline comparison network, and conduct quantitative analysis using the evaluation indicators of mean precision (mAP) and average precision (AP, including APs with IOUs of 0.50 and 0.75, recorded as AP 50 and AP 75 ) [1].…”

Section: Algorithm Feasibility Verification Analysismentioning

confidence: 99%

Lightweight Saliency Target Intelligent Detection based on Multi-scale Feature Adaptive Fusion

Zhu

2024

SCPE

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In order to solve the problems of small targets, variable shooting angles, and heights in drone images, the author proposes an adaptive drone target intelligent detection algorithm based on multi-scale feature fusion. The results show that after adding a deconvolution cascade structure to the network, mAP increased by about 2.5 percentage points and AP$^{50}$ increased by about 3 percentage points. Compared with Method 3, Method 4 uses GA-RPN instead of RPN, and when the IOU is 75, the AP increases by 3.5 percentage points, reflecting that the target prediction candidate boxes generated using semantic features adaptively match better than the manually designed target candidate boxes. This indicates that the proposed target detection framework has better classification ability and higher frame regression accuracy. Multi scale adaptive candidate regions are used to generate fused features of different scales generated by the network, weighted fused multi-scale features are used for target prediction, and semantic features are used to guide the network to adaptively generate target candidate frames, greatly enhancing the feature expression ability of various targets and improving the detection accuracy of aerial targets.

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Section: Algorithm Feasibility Verification Analysismentioning

confidence: 99%

Lightweight Saliency Target Intelligent Detection based on Multi-scale Feature Adaptive Fusion

Zhu

2024

SCPE

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“…The BCEWithLogits loss function is utilized in YOLOV5 for both object loss and classification loss, and CIoU is used as bounding box loss. The bounding box loss is primarily used to locate the prediction target in the image [31]. The traditional IoU loss [32] only works when the bounding boxes intersect.…”

Section: E Improved Loss Functionmentioning

confidence: 99%

TTD-YOLO: A Real-Time Traffic Target Detection Algorithm Based on YOLOV5

Xia,

Li,

Huang

et al. 2024

IEEE Access

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“…Tsung et al [14][15] introduces the Common Objects in Context (COCO) dataset, a widely used benchmark for object detection and image segmentation. It's essential for evaluating and benchmarking object recognition models.…”

Section: Literature Surveymentioning

confidence: 99%

Object Recognition in AI

Mohan

2023

IJSREM

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Object Recognition Using AI is a pioneering concept at the crossroads of artificial intelligence and computer vision, devoted to automating the identification and localization of objects within images and video streams. This paradigm harnesses machine learning algorithms to emulate human visual cognition, empowering systems to discern and precisely pinpoint diverse objects, transcending traditional image analysis. The essence of Object Recognition Using AI lies in its transformative impact across a multitude of applications. In retail, it revolutionizes inventory management by autonomously recognizing products on shelves. Within the security domain, it enhances surveillance systems, enabling real-time tracking of individuals and objects. In the automotive landscape, it empowers self-driving vehicles to interpret their surroundings, safeguarding passengers and pedestrians alike. Our research endeavors to advance the field of object recognition through the implementation of Mask R-CNN, a state-of-the-art deep learning architecture. We aim to achieve highly accurate object detection and segmentation by leveraging the power of convolutional neural networks. In pursuit of this, we conducted comprehensive experiments and evaluations. Our results showcase the effectiveness of Mask R-CNN in various real-world scenarios, with impressive precision and recall rates. In conclusion, this study contributes valuable insights and tools to the realm of computer vision, enabling enhanced object recognition applications. Keywords:Object Recognition, Mask R-CNN, Deep Learning, Convolutional Neural Networks, Computer Vision.

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Research on Object Detection Method Based on FF-YOLO for Complex Scenes

Cited by 13 publications

References 25 publications

Lightweight Saliency Target Intelligent Detection based on Multi-scale Feature Adaptive Fusion

Lightweight Saliency Target Intelligent Detection based on Multi-scale Feature Adaptive Fusion

TTD-YOLO: A Real-Time Traffic Target Detection Algorithm Based on YOLOV5

Object Recognition in AI

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