Research on Pedestrian Detection Model and Compression Technology for UAV Images

Liu, Xihao; Wang, Chengbo; Liu, Li

doi:10.3390/s22239171

Cited by 5 publications

(3 citation statements)

References 27 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The advantages of these methods lie in their ability to effectively extract high-level semantic information, exhibiting strong generalization ability and robustness. However, they require a large amount of annotated data and computational resources, and the detection accuracy for small targets still needs improvement [22][23][24][25][26][27][28][29][30][31][32][33][34][35].…”

Section: Methods Based On Deep Learningmentioning

confidence: 99%

TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

Zhao

Sun

et al. 2023

Drones

View full text Add to dashboard Cite

Drone detection is a significant research topic due to the potential security threats posed by the misuse of drones in both civilian and military domains. However, traditional drone detection methods are challenged by the drastic scale changes and complex ambiguity during drone flight, and it is difficult to detect small target drones quickly and efficiently. We propose an information-enhanced model based on improved YOLOv5 (TGC-YOLOv5) for fast and accurate detection of small target drones in complex environments. The main contributions of this paper are as follows: First, the Transformer encoder module is incorporated into YOLOv5 to augment attention toward the regions of interest. Second, the Global Attention Mechanism (GAM) is embraced to mitigate information diffusion among distinct layers and amplify the global cross-dimensional interaction features. Finally, the Coordinate Attention Mechanism (CA) is incorporated into the bottleneck part of C3, enhancing the extraction capability of local information for small targets. To enhance and verify the robustness and generalization of the model, a small target drone dataset (SUAV-DATA) is constructed in all-weather, multi-scenario, and complex environments. The experimental results show that based on the SUAV-DATA dataset, the AP value of TGC-YOLOv5 reaches 0.848, which is 2.5% higher than the original YOLOv5, and the Recall value of TGC-YOLOv5 reaches 0.823, which is a 3.8% improvement over the original YOLOv5. The robustness of our proposed model is also verified on the Real-World open-source image dataset, achieving the best accuracy in light, fog, stain, and saturation pollution images. The findings and methods of this paper have important significance and value for improving the efficiency and precision of drone detection.

show abstract

Section: Methods Based On Deep Learningmentioning

confidence: 99%

TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

Zhao

Sun

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…The Neck layer connects the Backbone and the Head, serving as a feature fusion layer [15] . The Neck layer can be implemented using several different methods, such as FPN (Feature Pyramid Network) or PAN (Path Aggregation Network).…”

Section: Yolov7mentioning

confidence: 99%

Pedestrian multi-object tracking based on YOLOv7 and BoT-SORT

Li,

et al. 2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

View full text Add to dashboard Cite

As a crucial component in the realm of computer vision, multi-object tracking has garnered widespread application in areas such as autonomous driving, smart transportation, and surveillance technology.Based on YOLOv7 and BoT-SORT algorithms, this paper followed TBD (track by detection) framework. First, YOLOv7 served as the detector tasked with identifying the target and specifying its location within the frame. Then, the BoT-SORT algorithm was used to track the target based on the target detection results. The results are then tested on the MOT20 dataset. The experimental data show that on MOT20 data set, the accuracy of multi-object tracking reaches 69.36%, and the accuracy of multi-object tracking reaches 79.11%, which effectively solves the problems of target occlusion and frequent identity switching. The experiment proves that it still has good tracking effect under dim lighting and crowded complex scenes. It can achieve better tracking for the non-rigid target of pedestrians, and has higher accuracy than YOLOv7-DeepSORT and YOLOv7-ByteTrack.

show abstract

“…In 2017, AlphaGo's consecutive victories against human players brought AI into the public eye, sparking a wave of interest. Today, AI has found widespread applications in various fields such as small object detection [1], speech recognition [2], image classification g [3], and more. Currently, most AI computational tasks rely on deployment on cloud and other large-scale computing platforms, but the significant physical distance between these resource-intensive platforms and smart endpoints limits the convenience of AI.…”

Section: Introductionmentioning

confidence: 99%

Research on Convolutional Neural Network Inference Acceleration and Performance Optimization for Edge Intelligence

Liang,

Tan,

Xie

et al. 2023

Preprint

View full text Add to dashboard Cite

In recent years, Edge Intelligence (EI) has emerged, combining edge computing with AI, specifically deep learning, to run AI algorithms directly on edge devices. In practical applications, EI faces challenges related to computational power, power consumption, size, and cost, with the primary challenge being the trade-off between computational power and power consumption. This has rendered traditional computing platforms unsustainable, making heterogeneous parallel computing platforms a crucial pathway for implementing EI. In our research, we leveraged the Xilinx Zynq 7000 heterogeneous computing platform, employed High-Level Synthesis (HLS) for design, and implemented two different accelerators for LeNet-5 using loop unrolling and pipelining optimization techniques. The experimental results show that when running at a clock speed of 100 MHz, the PIPELINE accelerator, compared to the UNROLL accelerator, experiences an 8.09% increase in power consumption but speeds up by 14.972 times, making the PIPELINE accelerator superior in performance. Compared to the CPU, the PIPELINE accelerator reduces power consumption by 91.37% and speeds up by 70.387 times, while compared to the GPU, it reduces power consumption by 93.35%.This study provides two different optimization schemes for edge intelligence applications through design and experimentation, and demonstrates the impact of different quantization methods on FPGA resource consumption. These experimental results can provide a reference for practical applications, thereby providing a reference hardware acceleration scheme for edge intelligence applications.

show abstract

Research on Pedestrian Detection Model and Compression Technology for UAV Images

Cited by 5 publications

References 27 publications

TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism

Pedestrian multi-object tracking based on YOLOv7 and BoT-SORT

Research on Convolutional Neural Network Inference Acceleration and Performance Optimization for Edge Intelligence

Contact Info

Product

Resources

About