An improved Haar-like feature for efficient object detection

The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination.

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“…After finishing the round of iteration, to train the following weak classifier [38], the weight of each sample is updated by Equation (9):

D_{t + 1} = \frac{D_{t} (x_{i})}{Z_{t}} \times {\begin{matrix} e^{- α_{t}} & i f y_{i} = h_{t} (x_{i}) \\ e^{α_{t}} & o t h e r w i s e \end{matrix}

where

Z_{t}

is a normalization factor which enables

D_{t + 1}

to be distributed and calculated by Equation (10):

Z_{t} = \sum_{j = 1}^{N} D_{t} (x_{i})

…”

Section: Methodsmentioning

confidence: 99%

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components

Luo

Tang

Zou

et al. 2016

Sensors

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“…The proposed result outperformed in terms of low false face detection rate, low in-plane rotation error and speed performance. The author in [15] proposed an improved Haarlike feature so called Haar Contrast Feature, which efficiently for object detection under various illuminations with the Haar Wavelet based. The LoG can be represented by Haar Wavelet which proposed by [16].…”

Section: Related Workmentioning

confidence: 99%

The Utilization of Feature based Viola-Jones Method for Face Detection in Invariant Rotation

Soon¹,

Basari²,

Ibrahim³

et al. 2018

ijacsa

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Faces in an image consists of complex structures in object detection. The components of a face, which includes the eyes, nose and mouth of a person differs from that of ordinary objects, thus making face detecting a complex process. Some of the challenges encounter posed in face detection of unconstrained images includes background variation, pose variation, facial expression, occlusion and noise. Current research of Viola-Jones (V-J) face detection is limited to only 45 degrees in-plane rotation. This paper proposes only one technique for the V-J detection face in unconstrained images, which V-J face detection with invariant rotation. The technique begins by rotating the given image file with each step 30 degrees until 360 degrees. Each step of adding 30 degrees from origin, V-J face detection is applied, which covers more angles of a rotated face in unconstrained images. Robust detection in rotation invariant used in the above techniques will aid in the detecting of rotated faces in images. The images that have been utilized for testing and evaluation in this paper are from CMU dataset with 12 rotations on each image. Therefore, there are 12 test patterns generated. These images have been measured through the correct detection rate, true positive and false positive. This paper shows that the proposed V-J face detection technique in unconstrained images have the ability to detect rotated faces with high accuracy in correct detection rate. To summarize, V-J face detection in unconstrained images with proposed variation of rotation is the method utilized in this paper. This proposed enhancement improves the current V-J face detection method and further increase the accuracy of face detection in unconstrained images.

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“…Since then Haar-like features have been popularly employed for object recognition due to its efficiency in the domain of face detection. It is illumination invariant because the mutual ordinal relationship of different pixel within the region is not affected by the varying illumination conditions (Park & Hwang, 2014). The three types of basis Haarlike features are presented in Fig.…”

Section: 4mentioning

confidence: 99%

“…In which, H(x) and h t (x) denote the function of strong classifier and weak classifier, T is the number of weak classifiers or features, a is the weight of weak classifier and it updated in each round of iteration (Park & Hwang 2014). The update rule of a is…”

Section: Adaboost Algorithmmentioning

confidence: 99%

Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis

Zhao

Gong

Zhou

et al. 2016

Biosystems Engineering

131

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An improved Haar-like feature for efficient object detection

Cited by 51 publications

References 23 publications

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components

The Utilization of Feature based Viola-Jones Method for Face Detection in Invariant Rotation

Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis

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