onnx转rknn推理中的filter_boxes函数问题

def filter_boxes(boxes, box_confidences, box_class_probs):
    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!

        # Arguments
            boxes: ndarray, boxes of objects.
            box_confidences: ndarray, confidences of objects.
            box_class_probs: ndarray, class_probs of objects.

        # Returns
            boxes: ndarray, filtered boxes.
            classes: ndarray, classes for boxes.
            scores: ndarray, scores for boxes.
    """
    boxes = boxes.reshape(-1, 4)
    box_confidences = box_confidences.reshape(-1)
    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])

    _box_pos = np.where(box_confidences >= OBJ_THRESH)
    boxes = boxes[_box_pos]
    box_confidences = box_confidences[_box_pos]
    box_class_probs = box_class_probs[_box_pos]

    class_max_score = np.max(box_class_probs, axis=-1)
    classes = np.argmax(box_class_probs, axis=-1)
    _class_pos = np.where(class_max_score >= OBJ_THRESH)

    boxes = boxes[_class_pos]
    classes = classes[_class_pos]
    scores = (class_max_score* box_confidences)[_class_pos]

    return boxes, classes, scores


def filter_boxes(boxes, box_confidences, box_class_probs):
    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!

    # Arguments
        boxes: ndarray, boxes of objects.
        box_confidences: ndarray, confidences of objects.
        box_class_probs: ndarray, class_probs of objects.

    # Returns
        boxes: ndarray, filtered boxes.
        classes: ndarray, classes for boxes.
        scores: ndarray, scores for boxes.
    """

    box_classes = np.argmax(box_class_probs, axis=-1)
    box_class_scores = np.max(box_class_probs, axis=-1)
    pos = np.where(box_confidences[..., 0] >= BOX_THESH)

    boxes = boxes[pos]
    classes = box_classes[pos]
    scores = box_class_scores[pos]
    return boxes, classes, scores

第一个函数代码是rknn-toolkit2\examples\onnx\yolov5\test.py里的，第二个函数代码是网上部分人使用的，请问第二个函数和第一个有什么区别，为什么采用第二个函数输出框的置信度高一些。