Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

Adami, Davide; Ojo, Mike; Giordano, Stefano

doi:10.1109/access.2021.3114503

Cited by 31 publications

(11 citation statements)

References 48 publications

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“…Adami et al [60] developed a smart agriculture application to protect crops from animals by repelling them through generated ultrasounds. They deployed and evaluated the performance of YOLOv3 and YOLOv3-tiny (a light version of YOLOv3) on different edge computing devices to detect deer and wild boar.…”

Section: Animal Detection Systemsmentioning

confidence: 99%

An Accurate and Fast Animal Species Detection System for Embedded Devices

Ibraheam

Gebali

2023

IEEE Access

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Encounters between humans and wildlife often lead to injuries, especially in remote wilderness regions, and highways. Therefore, animal detection is a vital safety and wildlife conservation component that can mitigate the negative impacts of these encounters. Deep learning techniques have achieved the best results compared to other object detection techniques; however, they require many computations and parameters. A lightweight animal species detection model based on YOLOv2 was proposed. It was designed as a proof of concept of and as a first step to build a real-time mitigation system with embedded devices. Multi-level features merging is employed by adding a new pass-through layer to improve the feature extraction ability and accuracy of YOLOv2. Moreover, the two repeated 3 × 3 convolutional layers in the seventh block of the YOLOv2 architecture are removed to reduce computational complexity, and thus increase detection speed without reducing accuracy. Animal species detection methods based on regular Convolutional Neural Networks (CNNs) have been widely applied; however, these methods are difficult to adapt to geometric variations of animals in images. Thus, a modified YOLOv2 with the addition of deformable convolutional layers (DCLs) was proposed to resolve this issue. Our experimental results show that the proposed model outperforms the original YOLOv2 by 5.0% in accuracy and 12.0% in speed. Furthermore, our analysis shows that the modified YOLOv2 model is more suitable for deployment than YOLOv3 and YOLOv4 on embedded devices.

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Section: Animal Detection Systemsmentioning

confidence: 99%

An Accurate and Fast Animal Species Detection System for Embedded Devices

Ibraheam

Gebali

2023

IEEE Access

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“…In another study, Adami, D., Ojo, M. O. and Giordano, S. (2021) 31 compared CNNs to develop a solution that combines edge and cloud computing with computer vision to safely deter animals such as wild boars and deer from agricultural areas on farms. The computer vision system interacts with the edge module through devices specialized in deep learning processing (Intel Movidius Neural Compute Stick (NCS) and NVIDIA Jetson Nano) coupled to a Raspberry Pi Model 3 B+.…”

Section: Related Workmentioning

confidence: 99%

Evaluating YOLO-based Object Detectors for Detecting Road-Killed Endangered Brazilian Animals

Ferrante

Nakamura

Sampaio

et al. 2023

Preprint

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Wildlife roadkill is a recurring, dangerous problem that affects both humans and animals and has received increasing attention from environmentalists worldwide. Addressing this problem is difficult due to the high investments required in road infrastructure to effectively reduce wildlife vehicle collisions. Despite recent applications of machine learning techniques in low-cost and economically viable detection systems, e.g., for alerting drivers about the presence of animals and collecting statistics on endangered animal species, the success and wide adoption of these systems depend heavily on the availability of data for system training. The lack of training data negatively impacts the feature extraction of machine learning models, which is crucial for successful animal detection and classification. In this paper, we evaluate the performance of several state-of-the-art object detection models on limited data for model training. The selected models are based on the YOLO architecture, which is well-suited for and commonly used in real-time object detection. These include the YoloV4, Scaled-YoloV4, YoloV5, YoloR, YoloX, and YoloV7 models. We focus on Brazilian endangered animal species and use the BRA-Dataset for model training. We also assess the effectiveness of data augmentation and transfer learning techniques in our evaluation. The models are compared using summary metrics such as precision, recall, mAP, and FPS and are qualitatively analyzed considering classic computer vision problems. The results show that the architecture with the best results against false negatives is Scaled-YoloV4, while the best FPS detection score is the nano version of YoloV5.

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“…At present, object detection technology is used in many fields in combination with object detection, such as in forest fire detection [9], identification of insulator defects on pylons [10], and aerial vehicle detection [11]. At the same time, there have been many studies on object detection for wildlife detection, such as O-YOLOv2, YOLOv2 [12], YOLOv3, Tiny-YOLOv3 [13], YOLOv4-uw [14], Faster R-CNN, Modified Faster R-CNN, RetinaNet [15], CenterNet, improved CenterNet [16], and other models, the performances of which are shown in Table 1. Although many models have high detection accuracy, the large scale of the models and the large number of parameters leads to their ability to perform real-time detection in application being insufficient.…”

Section: Related Workmentioning

confidence: 99%

Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5

Namiki

Suzuki

et al. 2022

Applied Sciences

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With the development of science and technology, the traditional industrial structures are constantly being upgraded. As far as drones are concerned, an increasing number of researchers are using reinforcement learning or deep learning to make drones more intelligent. At present, there are many algorithms for object detection. Although many models have a high accuracy of detection, these models have many parameters and high complexity, making them unable to perform real-time detection. Therefore, it is particularly important to design a lightweight object detection algorithm that is able to meet the needs of real-time detection using UAVs. In response to the above problems, this paper establishes a dataset of six animals in grassland from different angles and during different time periods on the basis of the remote sensing images of drones. In addition, on the basis of the Yolov5s network model, a lightweight object detector is designed. First, Squeeze-and-Excitation Networks are introduced to improve the expressiveness of the network model. Secondly, the convolutional layer of branch 2 in the BottleNeckCSP structure is deleted, and 3/4 of its input channels are directly merged with the results of branch 1 processing, which reduces the number of model parameters. Next, in the SPP module of the network model, a 3 × 3 maximum pooling layer is added to improve the receptive field of the model. Finally, the trained model is applied to NVIDIA-TX2 processor for real-time object detection. After testing, the optimized YOLOv5 grassland animal detection model was able to effectively identify six different forms of grassland animal. Compared with the YOLOv3, EfficientDet-D0, YOLOv4 and YOLOv5s network models, the mAP_0.5 value was improved by 0.186, 0.03, 0.007 and 0.011, respectively, and the mAP_0.5:0.95 value was improved by 0.216, 0.066, 0.034 and 0.051, respectively, with an average detection speed of 26 fps. The experimental results show that the grassland animal detection model based on the YOLOv5 network has high detection accuracy, good robustness, and faster calculation speed in different time periods and at different viewing angles.

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Design, Development and Evaluation of an Intelligent Animal Repelling System for Crop Protection Based on Embedded Edge-AI

Cited by 31 publications

References 48 publications

An Accurate and Fast Animal Species Detection System for Embedded Devices

An Accurate and Fast Animal Species Detection System for Embedded Devices

Evaluating YOLO-based Object Detectors for Detecting Road-Killed Endangered Brazilian Animals

Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5

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