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case_images: Areca palm plantation at the experimental site labeled with red quadrates. The blue semitransparent box covers a coconut palm (part of the background).
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case_images: F1
case_images: An example image is shown in Figure 1 (For better illustration, this image was taken from a much higher altitude for readers who have no idea of what an areca plantation is like). As shown in Figure 1, in most areca plantations, the contrast between foreground and background was not very obvious.
case_images: Network training and deployment on Neural Compute Stick 2 (NCS2).
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case_images: F2
case_images: In this study, the model was trained on a PC, and tested and used on NCS2, as described in Figure 2.
case_images: Bounding boxes with dimension priors and location prediction for YOLOv3.
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case_images: F3
case_images: The YOLOv3-Tiny framework, s: stride, c: channel.
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case_images: F4
case_images: The basic YOLOv3-Tiny architecture is shown in Figure 4.
case_images: The ResBlock in PeeleNet.
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case_images: F5
case_images: The ResBlock component proposed in PeleeNet (Wang et al.,), as in Figure 5, was used to compare with the original neck.
case_images: The compact MobileNet v2 backbone.
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case_images: F6
case_images: Figure 6 shows a compact version of MobileNet v2's backbone, and the bottleneck module is as defined in the original study.
case_images: (A) The standard channel shuffle operation in ShuffleNet; (B) channel reorganization operation in our study.
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case_images: F7
case_images: There were two reasons, the first is that the Channel Shuffle operation (as shown in Figure 7A) was not supported by the NCS2 hardware, therefore, Channel Reorganization was used to achieve the same or similar effect, (as shown in Figure 7B); Second, in this study, we found that in MobileNet v2 (as shown in Figures 8A,C), an activation layer followed a 3 x 3 depth-wise convolutional layer instead of a 1 x 1 one, and it showed higher accuracy (as shown in Figures 8B,D).
case_images: Building Blocks of ShuffleNet v2 and this study. (A) the basic ShuffleNet v2 unit; (B) the ShuffleNet v2 unit for spatial down sampling (2x); (C) our basic unit; (D) our unit for spatial down sampling (2x).
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case_images: F8
case_images: There were two reasons, the first is that the Channel Shuffle operation (as shown in Figure 7A) was not supported by the NCS2 hardware, therefore, Channel Reorganization was used to achieve the same or similar effect, (as shown in Figure 7B); Second, in this study, we found that in MobileNet v2 (as shown in Figures 8A,C), an activation layer followed a 3 x 3 depth-wise convolutional layer instead of a 1 x 1 one, and it showed higher accuracy (as shown in Figures 8B,D).
case_images: The ShuffleNet v2 backbone. Each line describes a sequence of one or more identical (modulo stride) layers, repeating for n times. All layers in the same sequence have the same number c of output channels. The first layer of each sequence has a stride s and all others use stride 1. All spatial convolutions use 3 x 3 kernels.
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case_images: F9
case_images: Definition of Intersection of Union (IoU).
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case_images: F10
case_images: TP represents the true positives, FP is the false positives, and FN denotes the false negatives. The definition of IoU is shown in Figure 10, which measures the intersection area of the predicted object boundary box and the ground truth, thereby evaluating the location accuracy of the predicted boundary box of the prediction.