Where's your head at? Detecting the orientation and position of pigs with rotated bounding boxes

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Pig detection in real production environments is a challenging task due to the variations of housing system and dynamic background. Though considerable progress has been made, for practical settings, there still existing challenges for densely housed pigs as they are often arbitrarily arranged at varying orientations in presence of lens distortion, overlap, occlusion, and motion blur. In this paper, we propose a rotated and oriented bounding box detector for fast and accurate predict the location and orientation of each animal. The key point is to parameterize pigs’ geometric parameters (body centre, body length, body width, orientation) with an orientated bounding box (box centre, long edge, short edge and direction vector). To further improve the performance on video object detection, a fast sequential non-Maximum Suppression (FastSeq-NMS) method is proposed by making used of the orientation and temporal information. To quantitative evaluate the proposed method, 3123 images from 27 different pens were selected as training and validation sets, video clips from three new environments were selected as test set. Our lightweight model (1.7 M) achieves 99.21 Average Precision (AP@0.5) on validation set, and 96.54 AP@0.5 on test set, further improved to 97.41 AP@0.5 with the proposed NMS method. The experiments show the effectiveness of the proposed method. More information available online: https://gitlab.kuleuven.be/m3-biores/public/m3pig.