Mixed YOLOv3-LITE: A Lightweight Real-Time Object Detection Method

Zhao, Haipeng; Zhou, Yang; Zhang, Long; Peng, Yangzhao; Hu, Xuemei; Peng, Hua; Cai, Xinyue

doi:10.3390/s20071861

Cited by 81 publications

(43 citation statements)

References 21 publications

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“…These frames were manually annotated with more than 2.6 million bounding boxes of the containing objects. UWS-YOLO was trained with VisDrone2019 and the result was compared with SlimYOLOv3 and Mixed YOLOv3-LITE (Zhao et al 2020) as compact, accurate architectures suitable for UAV applications in real-time. As apparent from the results in Table 5, UWS-YOLO has twice the accuracy of SlimYOLOv3 with the Speed of 67.0 FPS on our platform (NVIDIA Titan).…”

Section: Results With Visdrone2019mentioning

confidence: 99%

Efficient Real-Time Human Detection Using Unmanned Aerial Vehicles Optical Imagery

Golkarnarenji

Martinez‐Alpiste

Wang

et al. 2021

International Journal of Remote Sensing

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Unmanned Aerial Vehicles (UAVs) are promising technologies within many different application scenarios including human detection in search and rescue and surveillance use cases, which have received considerable attention worldwide. However, adverse conditions, such as varying altitude, overhead camera placement, changing illumination and moving platform, impose challenges for highperformance yet cost-efficient human detection. To overcome these challenges, we propose a novel combination of dilated convolutions with Path Aggregation Network (PAN) as a new deep neural network-based human detection algorithm in real time. Furthermore, we establish a comprehensive human detection dataset with varying backgrounds, illuminations, and contrast and train the proposed machine-learning model on the collected dataset. Our approach achieves both high precision (88.0% mean Average Precision (mAP)) and real time (67.0 Frames Per Second (FPS)) on a commercial off-the-shelf PC platform. In terms of accuracy, the result is comparable to the standard You Only Look Once v3 (YOLOv3). However, the speed is twice as that of the standard YOLOv3. YOLOv4 is slightly more accurate (89.8%) than our approach. However, it is slower (38.0 versus 67.0 FPS) and has more Billion Floating-Point Operations (BFLOPS). The proposed algorithm has also trained with the VisDrone2019 dataset and compared with seven studies using this dataset. The results have further validated the effectiveness of the proposed approach. Moreover, the algorithm has been evaluated on an embedded system (Jetson AGX Xavier), which demonstrates the usefulness of this method on power-constrained devices. The proposed algorithm is fast, memory efficient, and computationally less expensive to achieve high detection performance. It is expected to contribute significantly to the wider use of UAV applications including search and rescue missions to locate missing people, and surveillance particularly for applications running on resource-constrained platforms, like smartphones or tablets. This proposed system is now being used in aerial drone system of Police of Scotland to help them locate and find missing and vulnerable people. The results of the project were broadcasted by BBC Scotland.

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Section: Results With Visdrone2019mentioning

confidence: 99%

Efficient Real-Time Human Detection Using Unmanned Aerial Vehicles Optical Imagery

Golkarnarenji

Martinez‐Alpiste

Wang

et al. 2021

International Journal of Remote Sensing

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“…e YOLOv3 algorithm is a typical one-stage object detection algorithm that combines the classification and target regression problems with an anchor box, thus achieving high efficiency, flexibility, and generalization performance [21]. Since the YOLOv3 was proposed, it has been used in various object detection tasks [22][23][24].…”

Section: Yolov3mentioning

confidence: 99%

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

Chen

et al. 2021

Computational Intelligence and Neuroscience

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Vision-based recognizing and positioning of electronic components on the PCB (printed circuit board) can improve the quality inspection efficiency of electronic products in the manufacturing process. With the improvement of the design and the production process, the electronic components on the PCB show the characteristics of small sizes and similar appearances, which brings challenges to visual object detection. This paper designs a real-time electronic component detection network through effective receptive field size and anchor size matching in YOLOv3. We make contributions in the following three aspects: (1) realizing the calculation and visualization of the effective receptive field size of the different depth layers of the CNN (convolutional neural network) based on gradient backpropagation; (2) proposing a modular YOLOv3 composition strategy that can be added and removed; and (3) designing a lightweight and efficient detection network by effective receptive field size and anchor size matching algorithm. Compared with the Faster-RCNN (regions with convolutional neural network) features, SSD (single-shot multibox detectors), and original YOLOv3, our method not only has the highest detection mAP (mean average precision) on the PCB electronic component dataset, which is 95.03%, the smallest parameter size of the memory, about 1/3 of the original YOLOv3 parameter amount, but also the second-best performance on FLOPs (floating point operations).

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“…As autonomous vehicles applications require lightweight algorithms to be executed in low-computation and low-powered GPUs, we selected Tiny-YOLOv3 with three output layers because it balances the portability with high accuracy and, after the application of our technique, high speed. Other publications such as [31,56,58] also base their work in Tiny-YOLOv3 due to its trade-off.…”

Section: Yolov3mentioning

confidence: 99%

A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3

Martinez‐Alpiste

Golkarnarenji

Wang

et al. 2021

Neural Comput & Applic

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This paper proposes an acceleration technique to minimise the unnecessary operations on a state-of-the-art machine learning model and thus to improve the processing speed while maintaining the accuracy. After the study of the main bottlenecks that negatively affect the performance of convolutional neural networks, this paper designs and implements a discarding technique for YOLOv3-based algorithms to increase the speed and maintain accuracy. After applying the discarding technique, YOLOv3 can achieve a 22% of improvement in terms of speed. Moreover, the results of this new discarding technique were tested on Tiny-YOLOv3 with three output layers on an autonomous vehicle for pedestrian detection and it achieved an improvement of 48.7% in speed. The dynamic discarding technique just needs one training process to create the model and thus execute the approach, which preserves accuracy. The improved detector based on the discarding technique is able to readily alert the operator of the autonomous vehicle to take the emergency brake of the vehicle in order to avoid collision and consequently save lives.

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Mixed YOLOv3-LITE: A Lightweight Real-Time Object Detection Method

Cited by 81 publications

References 21 publications

Efficient Real-Time Human Detection Using Unmanned Aerial Vehicles Optical Imagery

Efficient Real-Time Human Detection Using Unmanned Aerial Vehicles Optical Imagery

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3

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